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    "# Session 3: Unsupervised and Supervised Learning\n",
    "\n",
    "<p class=\"lead\">\n",
    "Parag K. Mital<br />\n",
    "<a href=\"https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow/info\">Creative Applications of Deep Learning w/ Tensorflow</a><br />\n",
    "<a href=\"https://www.kadenze.com/partners/kadenze-academy\">Kadenze Academy</a><br />\n",
    "<a href=\"https://twitter.com/hashtag/CADL\">#CADL</a>\n",
    "</p>\n",
    "\n",
    "\n",
    "<a name=\"learning-goals\"></a>\n",
    "# Learning Goals\n",
    "\n",
    "* Build an autoencoder w/ linear and convolutional layers\n",
    "* Understand how one hot encodings work\n",
    "* Build a classification network w/ linear and convolutional layers\n",
    "\n",
    "<!-- MarkdownTOC autolink=true autoanchor=true bracket=round -->\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [Unsupervised vs. Supervised Learning](#unsupervised-vs-supervised-learning)\n",
    "- [Autoencoders](#autoencoders)\n",
    "    - [MNIST](#mnist)\n",
    "    - [Fully Connected Model](#fully-connected-model)\n",
    "    - [Convolutional Autoencoder](#convolutional-autoencoder)\n",
    "    - [Denoising Autoencoder](#denoising-autoencoder)\n",
    "    - [Variational Autoencoders](#variational-autoencoders)\n",
    "- [Predicting Image Labels](#predicting-image-labels)\n",
    "    - [One-Hot Encoding](#one-hot-encoding)\n",
    "    - [Using Regression for Classification](#using-regression-for-classification)\n",
    "    - [Fully Connected Network](#fully-connected-network)\n",
    "    - [Convolutional Networks](#convolutional-networks)\n",
    "- [Saving/Loading Models](#savingloading-models)\n",
    "    - [Checkpoint](#checkpoint)\n",
    "    - [Protobuf](#protobuf)\n",
    "- [Wrap Up](#wrap-up)\n",
    "- [Reading](#reading)\n",
    "\n",
    "<!-- /MarkdownTOC -->\n",
    "\n",
    "<a name=\"introduction\"></a>\n",
    "# Introduction\n",
    "\n",
    "In the last session we created our first neural network.\n",
    "\n",
    "We saw that in order to create a neural network, we needed to define a cost function which would allow gradient descent to optimize all the parameters in our network <TODO: Insert animation of gradient descent from previous session>.  We also saw how neural networks become much more expressive by introducing series of linearities followed by non-linearities, or activation functions.  <TODO: Insert graphic of activation functions from previous session>.\n",
    "\n",
    "We then explored a fun application of neural networks using regression to learn to paint color values given x, y positions.  This allowed us to build up a sort of painterly like version of an image.\n",
    "\n",
    "In this session, we'll see how to use some simple deep nets with about 3 or 4 layers capable of performing unsupervised and supervised learning, and I'll explain those terms in a bit.  The components we learn here will let us explore data in some very interesting ways.\n",
    "\n",
    "<a name=\"unsupervised-vs-supervised-learning\"></a>\n",
    "# Unsupervised vs. Supervised Learning\n",
    "\n",
    "Machine learning research in deep networks performs one of two types of learning.  You either have a lot of data and you want the computer to reason about it, maybe to encode the data using less data, and just explore what patterns there might be.  That's useful for clustering data, reducing the dimensionality of the data, or even for generating new data.  That's generally known as unsupervised learning.  In the supervised case, you actually know what you want out of your data.  You have something like a label or a class that is paired with every single piece of data.  In this first half of this session, we'll see how unsupervised learning works using something called an autoencoder and how it can be extended using convolution..  Then we'll get into supervised learning and show how we can build networks for performing regression and classification.  By the end of this session, hopefully all of that will make a little more sense.  Don't worry if it doesn't yet!  Really the best way to learn is to put this stuff into practice in the homeworks.\n",
    "\n",
    "<a name=\"autoencoders\"></a>\n",
    "# Autoencoders\n",
    "\n",
    "<TODO: Graphic of autoencoder network diagram>\n",
    "\n",
    "An autoencoder is a type of neural network that learns to encode its inputs, often using much less data.  It does so in a way that it can still output the original input with just the encoded values.  For it to learn, it does not require \"labels\" as its output.  Instead, it tries to output whatever it was given as input.  So in goes an image, and out should also go the same image.  But it has to be able to retain all the details of the image, even after possibly reducing the information down to just a few numbers.\n",
    "\n",
    "We'll also explore how this method can be extended and used to cluster or organize a dataset, or to explore latent dimensions of a dataset that explain some interesting ideas.  For instance, we'll see how with handwritten numbers, we will be able to see how each number can be encoded in the autoencoder without ever telling it which number is which.\n",
    "\n",
    "<TODO: place teaser of MNIST video learning>\n",
    "\n",
    "But before we get there, we're going to need to develop an understanding of a few more concepts.\n",
    "\n",
    "First, imagine a network that takes as input an image.  The network can be composed of either matrix multiplications or convolutions to any number of filters or dimensions.  At the end of any processing, the network has to be able to recompose the original image it was input.\n",
    "\n",
    "In the last session, we saw how to build a network capable of taking 2 inputs representing the row and column of an image, and predicting 3 outputs, the red, green, and blue colors.  Instead if having 2 inputs, we'll now have an entire image as an input, the brightness of every pixel in our image.  And as output, we're going to have the same thing, the entire image being output.\n",
    "\n",
    "<a name=\"mnist\"></a>\n",
    "## MNIST\n",
    "\n",
    "Let's first get some standard imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style> .rendered_html code { \n",
       "    padding: 2px 4px;\n",
       "    color: #c7254e;\n",
       "    background-color: #f9f2f4;\n",
       "    border-radius: 4px;\n",
       "} </style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
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     "execution_count": 1,
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   ],
   "source": [
    "# imports\n",
    "%matplotlib inline\n",
    "# %pylab osx\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.cm as cmx\n",
    "# Some additional libraries which we'll use just\n",
    "# to produce some visualizations of our training\n",
    "from libs.utils import montage\n",
    "from libs import gif\n",
    "import IPython.display as ipyd\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Bit of formatting because I don't like the default inline code style:\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"<style> .rendered_html code { \n",
    "    padding: 2px 4px;\n",
    "    color: #c7254e;\n",
    "    background-color: #f9f2f4;\n",
    "    border-radius: 4px;\n",
    "} </style>\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we're going to try this with the MNIST dataset, which I've included a simple interface for in the `libs` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "from libs.datasets import MNIST\n",
    "ds = MNIST()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at what this returns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ds.<tab>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can see that there are a few interesting accessors.  ... we're not going to worry about the labels until a bit later when we talk about a different type of model which can go from the input image to predicting which label the image is.  But for now, we're going to focus on trying to encode the image and be able to reconstruct the image from our encoding.  let's take a look at the images which are stored in the variable `X`.  Remember, in this course, we'll always use the variable `X` to denote the input to a network. and we'll use the variable `Y` to denote its output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(ds.X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So each image has 784 features, and there are 70k of them.  If we want to draw the image, we're going to have to reshape it to a square.  28 x 28 is 784.  So we're just going to reshape it to a square so that we can see all the pixels arranged in rows and columns instead of one giant vector."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x10f7826d8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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FgRKZK9xNJAj2hM0uIh51BHrYEUBmFAnQB7oW2hZCqgAYLZmB3FQdgdg9JAj2jI1vI7Jh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\n4P8D2MXC6tHvBCsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f14a048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(ds.X[0].reshape((28, 28)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11fd3f828>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gmxcoxXKYB/1ZQr70Yrwv7OzskJqa+l5zzQlF8gI3NzcEBwcrJvyUKVNGxiG4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uP7779XXQtbYpjNZnI6nZSTk0PdunWjEydO8CXGihUr+LMre35tNpuK+NS2pUuX6rYxgpU1bb+i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\nDIQFFligiv8FYxrDNauyXTkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fcea9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's get the first 1000 images of the dataset and reshape them\n",
    "imgs = ds.X[:1000].reshape((-1, 28, 28))\n",
    "\n",
    "# Then create a montage and draw the montage\n",
    "plt.imshow(montage(imgs), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the mean of the dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11fdf50f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AEENDBBa8Gx41fo0QUuQih2cQHrR96Ku93qzvxQgsIsDjS+1Bo6D/MYDXbBlM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\nnnoaxXHCE2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fd137f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the mean across all images\n",
    "mean_img = np.mean(ds.X, axis=0)\n",
    "\n",
    "# Then plot the mean image.\n",
    "plt.figure()\n",
    "plt.imshow(mean_img.reshape((28, 28)), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x12135d1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w5b8BoNj/NTMgAQJznBFLRxaOLEbDgEC6ZFiiGqOp4GyFsarhfIUx1HAMFh4G3hh4a+Ar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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11fe03198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Take the std across all images\n",
    "std_img = np.std(ds.X, axis=0)\n",
    "\n",
    "# Then plot the std image.\n",
    "plt.figure()\n",
    "plt.imshow(std_img.reshape((28, 28)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So recall from session 1 that these two images are really saying whats more or less contant across every image, and what's changing.  We're going to try and use an autoencoder to try to encode everything that could possibly change in the image.\n",
    "\n",
    "<a name=\"fully-connected-model\"></a>\n",
    "## Fully Connected Model\n",
    "\n",
    "To try and encode our dataset, we are going to build a series of fully connected layers that get progressively smaller.  So in neural net speak, every pixel is going to become its own input neuron.  And from the original 784 neurons, we're going to slowly reduce that information down to smaller and smaller numbers.  It's often standard practice to use other powers of 2 or 10.  I'll create a list of the number of dimensions we'll use for each new layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dimensions = [512, 256, 128, 64]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we're going to reduce our 784 dimensions down to 512 by multiplyling them by a 784 x 512 dimensional matrix.  Then we'll do the same thing again using a 512 x 256 dimensional matrix, to reduce our dimensions down to 256 dimensions, and then again to 128 dimensions, then finally to 64.  To get back to the size of the image, we're going to just going to do the reverse.  But we're going to use the exact same matrices.  We do that by taking the transpose of the matrix, which reshapes the matrix so that the rows become columns, and vice-versa.  So our last matrix which was 128 rows x 64 columns, when transposed, becomes 64 rows x 128 columns.\n",
    "\n",
    "So by sharing the weights in the network, we're only really learning half of the network, and those 4 matrices are going to make up the bulk of our model.  We just have to find out what they are using gradient descent.\n",
    "\n",
    "We're first going to create `placeholders` for our tensorflow graph.  We're going to set the first dimension to `None`.  This is something special for placeholders which tells tensorflow \"let this dimension be any possible value\".  1, 5, 100, 1000, it doesn't matter.  We're going to pass our entire dataset in minibatches.  So we'll send 100 images at a time.  But we'd also like to be able to send in only 1 image and see what the prediction of the network is.  That's why we let this dimension be flexible in the graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# So the number of features is the second dimension of our inputs matrix, 784\n",
    "n_features = ds.X.shape[1]\n",
    "\n",
    "# And we'll create a placeholder in the tensorflow graph that will be able to get any number of n_feature inputs.\n",
    "X = tf.placeholder(tf.float32, [None, n_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to create a network which will perform a series of multiplications on `X`, followed by adding a bias, and then wrapping all of this in a non-linearity:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# let's first copy our X placeholder to the name current_input\n",
    "current_input = X\n",
    "n_input = n_features\n",
    "\n",
    "# We're going to keep every matrix we create so let's create a list to hold them all\n",
    "Ws = []\n",
    "\n",
    "# We'll create a for loop to create each layer:\n",
    "for layer_i, n_output in enumerate(dimensions):\n",
    "\n",
    "    # just like in the last session,\n",
    "    # we'll use a variable scope to help encapsulate our variables\n",
    "    # This will simply prefix all the variables made in this scope\n",
    "    # with the name we give it.\n",
    "    with tf.variable_scope(\"encoder/layer/{}\".format(layer_i)):\n",
    "\n",
    "        # Create a weight matrix which will increasingly reduce\n",
    "        # down the amount of information in the input by performing\n",
    "        # a matrix multiplication\n",
    "        W = tf.get_variable(\n",
    "            name='W',\n",
    "            shape=[n_input, n_output],\n",
    "            initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))\n",
    "\n",
    "        b = tf.get_variable(\n",
    "            name='b',\n",
    "            shape=[n_output],\n",
    "            dtype=tf.float32,\n",
    "            initializer=tf.constant_initializer(0.0))\n",
    "        \n",
    "        # Now we'll multiply our input by our transposed W matrix\n",
    "        # and add the bias\n",
    "        h = tf.nn.bias_add(\n",
    "            name='h',\n",
    "            value=tf.matmul(current_input, W),\n",
    "            bias=b)\n",
    "\n",
    "        # And then use a relu activation function on its output\n",
    "        current_input = tf.nn.relu(h)\n",
    "\n",
    "        # Finally we'll store the weight matrix so we can build the decoder.\n",
    "        Ws.append(W)\n",
    "\n",
    "        # We'll also replace n_input with the current n_output, so that on the\n",
    "        # next iteration, our new number inputs will be correct.\n",
    "        n_input = n_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we've created a series of multiplications in our graph which take us from our input of batch size times number of features which started as `None` x `784`, and then we're multiplying it by a series of matrices which will change the size down to `None` x `64`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 64)\n"
     ]
    }
   ],
   "source": [
    "print(current_input.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to get back to the original dimensions of the image, we're going to reverse everything we just did.  Let's see how we do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[128, 256, 512, 784]\n"
     ]
    }
   ],
   "source": [
    "# We'll first reverse the order of our weight matrices\n",
    "Ws = Ws[::-1]\n",
    "\n",
    "# then reverse the order of our dimensions\n",
    "# appending the last layers number of inputs.\n",
    "dimensions = dimensions[::-1][1:] + [ds.X.shape[1]]\n",
    "print(dimensions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for layer_i, n_output in enumerate(dimensions):\n",
    "    # we'll use a variable scope again to help encapsulate our variables\n",
    "    # This will simply prefix all the variables made in this scope\n",
    "    # with the name we give it.\n",
    "    with tf.variable_scope(\"decoder/layer/{}\".format(layer_i)):\n",
    "\n",
    "        # Now we'll grab the weight matrix we created before and transpose it\n",
    "        # So a 3072 x 784 matrix would become 784 x 3072\n",
    "        # or a 256 x 64 matrix, would become 64 x 256\n",
    "        W = tf.transpose(Ws[layer_i])\n",
    "\n",
    "        b = tf.get_variable(\n",
    "            name='b',\n",
    "            shape=[n_output],\n",
    "            dtype=tf.float32,\n",
    "            initializer=tf.constant_initializer(0.0))\n",
    "        \n",
    "        # Now we'll multiply our input by our transposed W matrix\n",
    "        # and add the bias\n",
    "        h = tf.nn.bias_add(\n",
    "            name='h',\n",
    "            value=tf.matmul(current_input, W),\n",
    "            bias=b)\n",
    "\n",
    "        # And then use a relu activation function on its output\n",
    "        current_input = tf.nn.relu(h)\n",
    "\n",
    "        # We'll also replace n_input with the current n_output, so that on the\n",
    "        # next iteration, our new number inputs will be correct.\n",
    "        n_input = n_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After this, our `current_input` will become the output of the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = current_input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the output of the network, we just need to define a training signal to train the network with.  To do that, we create a cost function which will measure how well the network is doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?,)\n"
     ]
    }
   ],
   "source": [
    "# We'll first measure the average difference across every pixel\n",
    "cost = tf.reduce_mean(tf.squared_difference(X, Y), 1)\n",
    "print(cost.get_shape())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then take the mean again across batches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cost = tf.reduce_mean(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now train our network just like we did in the last session.  We'll need to create an optimizer which takes a parameter `learning_rate`.  And we tell it that we want to minimize our cost, which is measuring the difference between the output of the network and the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create a session to manage the training in minibatches:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# %%\n",
    "# We create a session to use the graph\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll train:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0441059\n",
      "1 0.0370064\n",
      "2 0.0365971\n",
      "3 0.0326115\n",
      "4 0.0331688\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.animation.ArtistAnimation at 0x127fdce80>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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hQ4cAADExMZIbtuSZ7PbBBx8ovTgYGhpCKBQiNzdXqeO2BS0Q/+Pj4wMLCwviLbso5Zo7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\nRQsERVFS/R/nvKOVTVtD5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123ed1f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAFECAYAAACqFMx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123ed1fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Some parameters for training\n",
    "batch_size = 100\n",
    "n_epochs = 5\n",
    "\n",
    "# We'll try to reconstruct the same first 100 images and show how\n",
    "# The network does over the course of training.\n",
    "examples = ds.X[:100]\n",
    "\n",
    "# We'll store the reconstructions in a list\n",
    "imgs = []\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for epoch_i in range(n_epochs):\n",
    "    for batch_X, _ in ds.train.next_batch():\n",
    "        sess.run(optimizer, feed_dict={X: batch_X - mean_img})\n",
    "    recon = sess.run(Y, feed_dict={X: examples - mean_img})\n",
    "    recon = np.clip((recon + mean_img).reshape((-1, 28, 28)), 0, 255)\n",
    "    img_i = montage(recon).astype(np.uint8)\n",
    "    imgs.append(img_i)\n",
    "    ax.imshow(img_i, cmap='gray')\n",
    "    fig.canvas.draw()\n",
    "    print(epoch_i, sess.run(cost, feed_dict={X: batch_X - mean_img}))\n",
    "gif.build_gif(imgs, saveto='ae.gif', cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"ae.gif?0.6955654442392385\" width=\"500\" height=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipyd.Image(url='ae.gif?{}'.format(np.random.rand()),\n",
    "           height=500, width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"convolutional-autoencoder\"></a>\n",
    "## Convolutional Autoencoder\n",
    "\n",
    "To get even better encodings, we can also try building a convolutional network.  Why would a convolutional network perform any different to a fully connected one?  Let's see what we were doing in the fully connected network.  For every pixel in our input, we have a set of weights corresponding to every output neuron.  Those weights are unique to each pixel.  Each pixel gets its own row in the weight matrix.  That really doesn't make a lot of sense, since we would guess that nearby pixels are probably not going to be so different.  And we're not really encoding what's happening around that pixel, just what that one pixel is doing.\n",
    "\n",
    "In a convolutional model, we're explicitly modeling what happens around a pixel.  And we're using the exact same convolutions no matter where in the image we are.  But we're going to use a lot of different convolutions.\n",
    "\n",
    "Recall in session 1 we created a Gaussian and Gabor kernel and used this to convolve an image to either blur it or to accentuate edges.  Armed with what you know now, you could try to train a network to learn the parameters that map an untouched image to a blurred or edge filtered version of it.  What you should find is the kernel will look sort of what we built by hand.  I'll leave that as an excercise for you.\n",
    "\n",
    "But in fact, that's too easy really.  That's just 1 filter you would have to learn.  We're going to see how we can use many convolutional filters, way more than 1, and how it will help us to encode the MNIST dataset.\n",
    "\n",
    "To begin we'll need to reset the current graph and start over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import reset_default_graph\n",
    "reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# And we'll create a placeholder in the tensorflow graph that will be able to get any number of n_feature inputs.\n",
    "X = tf.placeholder(tf.float32, [None, n_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `X` is currently `[batch, height*width]`, we need to reshape it to a\n",
    "4-D tensor to use it in a convolutional graph.  Remember back to the first session that in order to perform convolution, we have to use 4-dimensional tensors describing the:\n",
    "\n",
    "`N x H x W x C`\n",
    "\n",
    "We'll reshape our input placeholder by telling the `shape` parameter to be these new dimensions.  However, since our batch dimension is `None`, we cannot reshape without using the special value `-1`, which says that the size of that dimension should be computed so that the total size remains constant.  Since we haven't defined the batch dimension's shape yet, we use `-1` to denote this\n",
    "dimension should not change size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tensor = tf.reshape(X, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now setup the first convolutional layer.  Remember from Session 2 that the weight matrix for convolution should be\n",
    "\n",
    "`[height x width x input_channels x output_channels]`\n",
    "\n",
    "Think a moment about how this is different to the fully connected network.  In the fully connected network, every pixel was being multiplied by its own weight to every other neuron.  With a convolutional network, we use the extra dimensions to allow the same set of filters to be applied everywhere across an image.  This is also known in the literature as weight sharing, since we're sharing the weights no matter where in the input we are.  That's unlike the fully connected approach, which has unique weights for every pixel.  What's more is after we've performed the convolution, we've retained the spatial organization of the input.  We still have dimensions of height and width.  That's again unlike the fully connected network which effectively shuffles or takes int account information from everywhere, not at all caring about where anything is.  That can be useful or not depending on what we're trying to achieve.  Often, it is something we might want to do after a series of convolutions to encode translation invariance.  Don't worry about that for now.  With MNIST especially we won't need to do that since all of the numbers are in the same position.\n",
    "\n",
    "Now with our tensor ready, we're going to do what we've just done with the fully connected autoencoder.  Except, instead of performing matrix multiplications, we're going to create convolution operations.  To do that, we'll need to decide on a few parameters including the filter size, how many convolution filters we want, and how many layers we want.  I'll start with a fairly small network, and let you scale this up in your own time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_filters = [16, 16, 16]\n",
    "filter_sizes = [4, 4, 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll create a loop to create every layer's convolution, storing the convolution operations we create so that we can do the reverse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "current_input = X_tensor\n",
    "\n",
    "# notice instead of having 784 as our input features, we're going to have\n",
    "# just 1, corresponding to the number of channels in the image.\n",
    "# We're going to use convolution to find 16 filters, or 16 channels of information in each spatial location we perform convolution at.\n",
    "n_input = 1\n",
    "\n",
    "# We're going to keep every matrix we create so let's create a list to hold them all\n",
    "Ws = []\n",
    "shapes = []\n",
    "\n",
    "# We'll create a for loop to create each layer:\n",
    "for layer_i, n_output in enumerate(n_filters):\n",
    "    # just like in the last session,\n",
    "    # we'll use a variable scope to help encapsulate our variables\n",
    "    # This will simply prefix all the variables made in this scope\n",
    "    # with the name we give it.\n",
    "    with tf.variable_scope(\"encoder/layer/{}\".format(layer_i)):\n",
    "        # we'll keep track of the shapes of each layer\n",
    "        # As we'll need these for the decoder\n",
    "        shapes.append(current_input.get_shape().as_list())\n",
    "\n",
    "        # Create a weight matrix which will increasingly reduce\n",
    "        # down the amount of information in the input by performing\n",
    "        # a matrix multiplication\n",
    "        W = tf.get_variable(\n",
    "            name='W',\n",
    "            shape=[\n",
    "                filter_sizes[layer_i],\n",
    "                filter_sizes[layer_i],\n",
    "                n_input,\n",
    "                n_output],\n",
    "            initializer=tf.random_normal_initializer(mean=0.0, stddev=0.02))\n",
    "\n",
    "        # Now we'll convolve our input by our newly created W matrix\n",
    "        h = tf.nn.conv2d(current_input, W,\n",
    "            strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "        # And then use a relu activation function on its output\n",
    "        current_input = tf.nn.relu(h)\n",
    "\n",
    "        # Finally we'll store the weight matrix so we can build the decoder.\n",
    "        Ws.append(W)\n",
    "\n",
    "        # We'll also replace n_input with the current n_output, so that on the\n",
    "        # next iteration, our new number inputs will be correct.\n",
    "        n_input = n_output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now with our convolutional encoder built and the encoding weights stored, we'll reverse the whole process to decode everything back out to the original image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[16, 16, 1] [4, 4, 4] [[None, 7, 7, 16], [None, 14, 14, 16], [None, 28, 28, 1]]\n"
     ]
    }
   ],
   "source": [
    "# We'll first reverse the order of our weight matrices\n",
    "Ws.reverse()\n",
    "# and the shapes of each layer\n",
    "shapes.reverse()\n",
    "# and the number of filters (which is the same but could have been different)\n",
    "n_filters.reverse()\n",
    "# and append the last filter size which is our input image's number of channels\n",
    "n_filters = n_filters[1:] + [1]\n",
    "\n",
    "print(n_filters, filter_sizes, shapes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# and then loop through our convolution filters and get back our input image\n",
    "# we'll enumerate the shapes list to get us there\n",
    "for layer_i, shape in enumerate(shapes):\n",
    "    # we'll use a variable scope to help encapsulate our variables\n",
    "    # This will simply prefix all the variables made in this scope\n",
    "    # with the name we give it.\n",
    "    with tf.variable_scope(\"decoder/layer/{}\".format(layer_i)):\n",
    "\n",
    "        # Create a weight matrix which will increasingly reduce\n",
    "        # down the amount of information in the input by performing\n",
    "        # a matrix multiplication\n",
    "        W = Ws[layer_i]\n",
    "\n",
    "        # Now we'll convolve by the transpose of our previous convolution tensor\n",
    "        h = tf.nn.conv2d_transpose(current_input, W,\n",
    "            tf.stack([tf.shape(X)[0], shape[1], shape[2], shape[3]]),\n",
    "            strides=[1, 2, 2, 1], padding='SAME')\n",
    "\n",
    "        # And then use a relu activation function on its output\n",
    "        current_input = tf.nn.relu(h)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have the reconstruction through the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = current_input\n",
    "Y = tf.reshape(Y, [-1, n_features])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can measure the cost and train exactly like before with the fully connected network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.0262561\n",
      "1 0.0264586\n",
      "2 0.0247447\n",
      "3 0.0236072\n",
      "4 0.0234347\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.animation.ArtistAnimation at 0x121138668>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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jR+q6Dx48IElJSdU+TxqcWhDk/267fU1ycjJTytzDw6NSdWTaFBQUQE9PT+7XRUZGQl9f\nH58+fao0YLRx40ZERUUhKysLAQEBnAa7kpKSxKpR8XVri09GjRoFLy8varcQ3dzcmBYnH0i6tcwG\nCwsLdOjQAa1btwZQ9ktMu6p8OdevX8eff/6J8+fPU9Ns0qQJTE1N4erqylqDU4IQiUQIDQ3F7Nmz\nmT/sw4cPzBukr6/Pez8zPDycczm48lJlFVm0aJHYACAbuLw50jA0NKx2HoW/vz/18yoCISEhrG7n\nScLW1haenp7o3Lkzs2/RokXw9fWloi8JmmNUISEhnCqQAxzvYixevBj169fHx48fERoaKrEoq0gk\nkvjae/fuiVmwDRs2DB4eHtWe8/vvv8fDhw+ZugTdunVDRkYGQkJCWP0NkrC3t0fnzp1x7NgxtGjR\nQqa4KtKyZUt8+vSJl362iooKnJ2dJSYfY2NjDB48GJcuXZJY04ENrVu3RmlpKVq0aCHza6q6Zmpq\nalBVVaX6npVTPllMmjab93LKlCnMILWxsTF0dHSoD+CyiasqrK2tYWBggOfPn8t8nQ8ePMj8397e\nvswBm8XQg0QOHjxIjh8/ToKCgsj79+8JIYS8f/+eBAUFyawhS//Q0NCQbNq0iZiZmZH8/Hxy4MAB\n6v22MWPGkM+fP3Pqt2ppaVVZbp3GlpKSQtLS0iRubdq0oXaeQYMGkQEDBsj1mtoagzAzM+MU1+bN\nm8Wm3HOZEl9b10tTU5OkpaWRIUOGyPwaabBuQRQWFoIQAg0NDRQUFODOnTsYMmQI2rdvj4sXL2Lg\nwIG4ePEinJyc2J5CItnZ2di9ezfmzp2LnTt34ocffqCqD5TVmeA6Ut2uXTt8+PCBVwfuw4cPY+HC\nhbzpl3P06FH06NEDqqqqKCoq4v18XHj69CmnKfgVu5u0K5bVBH5+frCwsKCixTpBfPjwAatWrYJI\nJEJJSQk6d+6MNm3awNraGmvXrkV8fDyMjY2rnKXIlmvXrvHuAsy173blyhVKkdQNsrOzqU7s4Qtv\nb+/aDqFWWbZsGaZMmYJt27ZR0WOdIExMTCQWYNXW1sb8+fM5BSVQ96jLxX6+5vTp07UdQq0ye/Zs\nqneIFGompYCAQM0iJAgBAQGpCAlCQEBAKkKCEPjPYG1tLXXejQA7hAQh8J/A0dERJ06c+E/VF60L\nCAlC4D/B8+fPOft2CFRGSBBSMDAwoDZVWdFo27YtPn78yItNPwA4OTnh1atXnDwdv0ZJSQlqamro\n06cPbxO5tLS0YGpqyixQfP/+PV69egUVFRXo6Oiw0tTT04OysjJevXpFtYivSCSCqamp2MZ24t//\nbHvMxsZG4iy5iIgIAMDevXvh5eVF1flIUcjOzsbo0aMRGxvLi35ycjLMzMyo6ZmYmOD169fw8fHB\nsWPHqOl+TceOHTFixAhs3boVQNnn48qVK7CwsICjo6Pc18rS0hL+/v5YvXo1zMzMqK1L6devH8zM\nzCotMoyNjYW6ujqMjIzw8OFDXL58WSa9/9kE4eLiwrzZALBz504cPXqUebxw4UJs2rSpNkLjxJo1\na/DTTz9x0khLS6vzTtYVOXv2LG+za7ds2YLvvvsO8fHxlY6xvVYzZszAqlWr8P79exohAgB8fHxg\nbGyMFy9eSLTc69y5M4qKiuDs7IyioiKZrledThDbtm3DnDlzkJWVRV07MjISkZGRUo+HhIQoZAuC\na3IAyroYgwcPVpgZsSKRCC9evOBtDOK7776jrmlpaSlxpe/79+/lsgssx87ODq6urpgxY4bU55S3\nGuRxl6rTn/6JEyciKysLenp6vPkjVgVrmy4F59atW0hJScG3337L2c6vIgcOHKCqBwCvX78We2xk\nZIS+ffvyVtqvQYMG6Nu3L/r27YumTZti1KhRcmsMGDBA4n42yUFeLCwscPXqVZnMb+p0gihHVVUV\nvr6+2Lx5s8T1H3ywfv16qW/i/wIHDx5EdnY29YHaESNGUNUDyn7h9+7dyzxWV1eHmZkZli1bRv1c\nmzdvxrRp02BmZgYzMzM8ffq0ypaovKxdu5bV69LT05Gbm1vlD+mcOXOwYsUKaGlpYdeuXSgpKale\nmJsLBF2qWhOvo6NDbty4QRo2bFjjHgO16a+oiHHVldgaNGhALl26RC2uyMhIUlRUxNlXNC0tTer1\nysnJYa2rra0tVo6xfGvXrh15+fIl6datm9TvjzQUogUBALm5uXB0dGT6mStWrICzszP181hZWSE6\nOhq6urrUtfnE0tJS4iAaF65evYoLFy5Q1eSDnj17IiIiAr/++iv+/vtvZn9mZiYMDQ2xevVquTW1\ntbXRrl07PH36lLlDkZOTA1VVVc6+omZmZrC2tq50vgMHDnDy08zLy0NGRgbatGnD7Dt//jxu3rwJ\nc3NzXLhwQe5xGoVJEBWZOXMmevbsSV136tSpaNKkiUKUZv+adu3aVSrxx5UdO3agW7duVDX54OzZ\nswCA6dOno3379mLHBg0aJLfe7Nmz8dtvv2H58uXYu3cv4zHBpbra1yxevBijR49mYjUxMYGPjw/W\nr1/PWTsxMRF//vkn5s+fj9atW3P2uKzTdzGqg2a5+3KioqIQFhaGFy9eUNfmEz7mLNAyHeGbDh06\nSKxbcvjwYTRv3hznzp2TSadfv3745Zdf0L59ezg7OyMtLQ2ZmZkAyjxTX7x4QeVWakhICPT09KCm\npgagrHV8/vx53L59m7VmeQ3Ys2fPMsWkaEx0U+gEQRNtbW0kJyfD3d2dWpEcgZohOTkZmpqaTKvv\ny5cvCAoKQqdOnZCamoqff/5ZJh0jIyPY2NhAS0sLBQUFYsd0dHRY1baUxtdu7/n5+Xj9+nWVJSCr\n4+TJk2Ums1+hp6cnVkKRDQrbxaCNp6cnLl++LPOHSUA+rK2tK/W7adK+fXuEhoYiNDQU48aNQ0hI\nCExNTcUs66vj0aNH6NGjR6XkAADbt29nnNRpU79+fYmO8LIybNgwifvDwsI43zb9T7Qgxo8fD29v\nb6auJBuOHDlSqVy6AD0kfelocuXKFc4+oNKqm2/evFlq7RQamJmZwc7OjvXrpQ2ahoWFITc3l7Uu\n8B9pQezatQu2tracNJYtW8a5XPx/iTlz5lC9Hq9evVLYa8yHc/rX3Lp1i9Pr//jjD9jZ2WH58uUA\ngOLiYkRFReHmzZucF6/9JxIEIYRTBgbKmpdnzpyhFJHis3TpUureCmPGjFFIvwa+3bxVVFQ4l9xT\nUlLCn3/+id9//x2xsbHUqn8p3rvFEzt37sTOnTtrOwwBAdYcPXpUbMEhDf4TLQgBAQF+EBKEgICA\nVIQEISAgIBUhQQgICEhFSBACAMpmEdKcKSjw30BIEAJQUlLCihUr0LFjRwDSzUzYoqqqivHjx2P8\n+PHM+gM+GT9+PBVD3A4dOjBxjx8/nqqPZk0yfvx4sRWe8qBwCUJXV5eZ9SjMfBRn+/btrF5XWlqK\nCRMmICEhAQA4rQmQxJ49e5CTk4OcnBzs3r0bS5YsoapfkR07dnBe6XvkyBH07NmTibtTp06V1joo\nCrNmzWI9k7XaBLFp0yZMmjQJ06dPZ/bl5eUhNDQUgYGBWLJkCT5//swci4mJwdSpUzFt2jROq9Mq\nMm/ePDg5OeHjx48YPnw4CCEYNGiQQkxu8vT0xJcvX9C4cWNez9O3b18qOhcuXMDs2bOp+G3Ex8dj\n9OjRiImJQUxMDEaPHo3Tp09j5syZFCLlhw8fPqB+/fpYvnw5E7e/vz+CgoJqOzS5SU9Px7t37/DP\nP/+wen21CaJr166YO3eu2L5jx47BwcEBYWFhsLe3R0xMDADg5cuXSEpKwtq1azF79mxs27aN00oy\nAMjIyMBff/2F0NBQJCcnAyibSjpkyBCIRCKUlpZi9uzZ8PT0lFt71qxZiIuLQ1xcHIYMGQIAuHjx\nIo4fPw4jIyMAZV9uPT09VrE7OjqipKQE7u7uUFNTw7Nnz5hj8fHxzHWriygpKVEpY/f333+LTa8u\nLi7G5cuXoauri8mTJ3PW54P4+Hh069ZNbAZlaWkpFQNjR0dHqZ/VX3/9lbN+RWTxnayKav/iFi1a\nQEtLS2xfcnIyvvnmGwCAh4cH/vrrL2a/m5sblJWVYWJiAlNTU6SmpnIKcPbs2Vi5cqXU415eXjA0\nNGTWwMvD8uXL4eXlBS8vLxw+fBgA0L9/f5w5c4b5m21sbKCurs4q9gULFiAwMFCiO3RYWBh8fHxY\n6Upi5MiRvHgwcuXrlufXnDhxAnPmzOHlnA8fPuS08pLLor/qWLx4MWxsbCQek3atuDBz5kxWjlrl\nsEqJHz58YKyx9PX1mbXt7969Y355gbLqVFztuXbu3IlDhw4hMDAQz549Y1oRFdm3b59cuj4+Pnj2\n7Fml5Jebm4vw8HDGktzR0ZGV7f7IkSOxf/9+bNiwQWz/s2fPMHv2bE4FXiTN2+/evbtCLVW/du0a\nMyhKk0ePHiEtLY1q95Ym48aNQ3h4uNTjd+7coXq+7du3Mz9+bKAySFkTFZXDwsLQuHFjODk5ie3v\n2LEj7ty5I3cfS0tLC+Hh4cjPz5d43MrKihm8kxczMzPs27ev0mDf06dPcfPmTURERHCyf5c0mj5h\nwgRYWlpS81zIysoSG1vig/T0dM6rcCvCV3mEKVOmYM2aNZx13rx5I/VY06ZNOS/Ppg2rxVr6+vrI\nyclh/i3voxsYGIi5MWVnZ8PAwECixr1793Dv3j3m8bBhw+Dh4cEmHACQu3RZ69at0bJlS+jo6Ei0\n//bz88Ply5cREhIid1zq6up4/PgxfH19Gd9Ba2trpKWl4datW1i5ciUyMjJYl1t79uyZxLicnZ3R\npk0bsYphXPDx8WHdDZL1mnXq1AmJiYmsziGNpk2bSr22bD9jzs7O6NChA1xdXTlEJh0PDw+0b98e\nZ8+epVaGT14OHjzI/N/e3r7sro0sdvSZmZnkp59+Yh7v3buXxMTEEEIIiYmJIZGRkYQQQtLS0khw\ncDApKioimZmZJCAggJSWllKxva+4hYaGEjU1NdYW4UZGRsTQ0FDq8d9//52TVbq/vz+5f/8+SUxM\nJImJiWT69OlEXV2dk116+WZpaUkSExPJixcvSGJiIlmyZAkBQJycnEjr1q056zdr1oxMmjSJk4ak\naxYSEsJcj9GjR1O5FpK2li1byhVXdVujRo3IihUriL6+Pm8x0y4T0K1bN5KYmEh0dXVler40qk0Q\n69atI/7+/sTX15d8//335MKFCyQ3N5csWrSITJ06lSxevJjk5eUxzz969CgJCAggQUFB5NatWzIn\nB1kThIGBAZk0aRLp378/b28Wn2+eq6srWblyJdW4Bg0aRMaPH08txrdv35LZs2dTu2YikYhMmjSJ\nfPz4sdoPrIGBAbVESuu9VFJSIvXq1eMtJgAkLi6OODs7U9NTU1MjUVFRMtfwkEa1XYzAwECJ+6XV\nbeTSLK2Ovn374tChQwgICMCJEyd4OQcA2NraIi8vj4orcEWuXr1aY9XB2PL1QDMNdHR0MGvWLPTq\n1QsfP36s8rkHDhxAbGxslQN50ujatSssLS2xe/dutqFKpHHjxggMDJT6XaDBtWvXcP36dWp63t7e\nMDMzQ2FhIScdhZpJ+csvv2DYsGHYsWMHb+cwNjZGVFQUWrduzYt+WlqaWJk4rqiqqsLd3Z1a0RxJ\nVaErMmrUKBw6dEjmL/Hnz5/h6+srk2V8+UQqNkyZMgW7du1i9dqqqHgnijaWlpZwcHCgpqeurg4X\nFxeMHj0anz594qSlUAkCqFxQl+2HSRpv3rzh5UMGlN2fz83NRUpKCjVNJSUlWFpaik3C4sKUKVNQ\nUFBQaaBsz549KCgowLlz5xAVFQU/Pz9MmzZNJs3i4mL8+eeflfZL2peZmYknT56wCx5lrRXaVFUx\nvU2bNpwHFXV1dWFiYsJJ42sKCwsxc+ZMidXD5UWhLOeuX7+OLl26MGsGPn/+zEsFZ76MVdXU1Dh7\nD1YkJycHmpqa1PS+fPkCe3t7bNq0ialYBZR5VI4ZM4Z5LFPh1wrMmjULd+/eRUFBAb799lternNM\nTAz1imsfPnyAnZ0dJk2ahH///Rdv3rzB7du3ERUVBV9fX85zLvbv3099XVH5tT18+DAzS5gNCpUg\npk6dik93PXM4AAAgAElEQVSfPjEVn2bNmsXLeS5fvlxtX5ktkyZN4kWXJk+ePEGvXr2o6y5fvhz+\n/v5QV1dHUlISpk6dSlV/27Zt1FuUQNl8jV69esHb2xt2dnZQV1fH7du3qRjDLliwAMuXL0ezZs0o\nRFoZLskBULAEAQCtWrVi/k+rWV2RlJQUbN++ndUkKWmEhITA3Nycml45XN28axpaczQkcerUKd60\nAX7KG44fPx5WVla1NvehOhRuDOLff/9lNsJxIVhVTJgwgepYwcKFC3nxQuArSQrUDFZWVrUdQpUo\nXIKoSWiOLAsIKCJCghAQEJCKkCAEBASkIiQIAQEBqQgJQkBAQCpCghCoFRYsWCB2y5o2K1aswKdP\nn3Dz5k3ezvG/gJAgahA3N7faDqFOMGbMGPzzzz+4e/cuL/pt27bFzJkzoaWlhXbt2vFyDtq4u7vz\nctv+a38WNggJogaxtbVFgwYN0L9//9oOpUrWrl0LLy8v3vT9/f0RFRXFi/avv/5apYdpXeXp06d4\n+vQpL9pc3LiFBFEDdOnSBaNGjcLOnTthYGCAzp0713ZIVdKrVy9cv34dBw8exOPHj6kuT//7778x\nfPhwanoVmTJlCsaNG0ddd8yYMXj8+DF13XJev36N169f86LN5XoLCaICqqqqaNCgAVVNPz8/REZG\nAihbDMXGBLcmaNiwIa5cuQJ7e3u8e/cOw4YNw9u3bxEcHExFv1GjRujQoQMvPht6enrQ1taGuro6\n1S9a06ZNkZqaij179oitl1i4cCHu379P7Tx8wmW2rZAgKtCoUSOpZjjyUm6c89133zH7unTpwkv9\nAxp06tQJffr0Edvn6uqKli1bokWLFpz1V65cyctybC0tLURERKBHjx5UdT08PDBp0iRERERUOpaY\nmIgOHTpQPR9fcFlU9p9JELRMWDIyMrB27VoqWo8ePaq0j4/aB7Q4cuSIxFWsjo6OrGs7ltO9e3dc\nu3aNelk/APjjjz+gqamJy5cvU9Ns164dXFxcsGjRIqxYsaLS8X///RdFRUVyaQ4cOLDKamUBAQFy\nx8k3CreaUxqjR4+molNYWMjJsORrvnbtLoePLwgAaGhoQFVVlbptur6+Pjp37izWCmLD+fPnxbww\nfvnlFxw9epTKnQwrKyvqbtM3b96Ueot0yJAh0NLSkntMorpaKHzU8uD6efjPtCDqElVVZqJR71IS\no0ePxqZNm6jr5uTkcE4Okli0aBGTHGxsbFhXzu7duzfNsKpl9OjR8PPzk9v3UktLCyNHjkSjRo14\nikwyTZo04fT6Op0gvvvuu2rrYtrb21MrWkuLffv2ISoqCo6OjpWOmZmZYeTIkQDKvAOjoqLQpUsX\nzueMiIjAqFGjOOtI0uWbbt26sZ6vsGTJkiprfJqamlK9Ljt37mRVyo4QgpKSEl4tCvigTncxdu3a\nhS9fvlT5nIcPH0rs69cm5f6CkkquHz9+nGkOFxYWYuLEiZydh/lk0KBB1Fywtm3bhp9//pkp1VjO\njh07OH1xqjJy0dHRQbt27Zi7SFyIiYmBnp4eKyPYz58/Izo6mnMMsuLj4wNNTU3s37+fk06dbkEU\nFhZW+8EpKSmRe7CIbz59+oRPnz5J9G1s3769WNL79OkTbx6YXFm/fj0MDQ2p6a1evVril6u4uJiV\nx2V1ODs7Q19fn3PNUg0NDfz2229o0qQJZ5fo6jh16hQVlzAVFRWoqHD//a/TCUKg9ujRowf127GN\nGzfG0qVLAZRVZTc1NeWsuXbtWvzyyy+V9nt7e+Obb74pKx/HkUaNGqFt27bw9vbmrFUdI0aMwLx5\n83g/j6zU6S7Gf43z58+je/futR1GtdjZ2aFLly64cuUKVd24uDimO/jnn39SueMSGRmJpk2bVtp/\n9epVnDp1ikrrcseOHRg8eHCVhXfrIitWrMDx48eRk5PDWkNIEDUI1xHlmkJdXR0ikUjiGApXym8h\nZ2dnU9OUtIaB1pfZysoKRUVFNZYcSktLkZ6ezlknNzcXkydP5pQcACFB1Cjldv11ndGjR+Pz58+1\nHUad4PTp0zXqHJ6Xl0dlMh0t+38hQdQg5f3vus7x48epVGVSdCZPngxLS8vaDqNWEQYpBSpx6dIl\nwU4fwKZNm6ClpVXbYdQq1bYgNm3ahBs3bkBPT48Z1T506BDOnz/PTGLy9fVF27ZtAZTdK46Pj4ey\nsjLGjRvHeQ6/gIBA7VFtgujatSu8vLwqVTju168f+vXrJ7bv5cuXSEpKwtq1a5GdnY3Fixfjt99+\ng0gkohu1gIBAjVBtF6NFixYSm1mSJjAlJyfDzc0NysrKMDExgampKVJTU+lEKiAgUOOwHqQ8ffo0\nEhISYG1tjTFjxqBevXp49+4dmjdvzjzHwMAA7969oxKogIBAzcMqQXh6emLIkCEQiUQ4cOAA9uzZ\ng++//552bAK1zI8//ggrKyvcuHEDBw4cqO1wBGoBVglCV1eX+X/37t0ZQw0DAwMxF93s7GwYGBhI\n1Lh3756YX8KwYcPg4eHBJhze+V+Lq9y4RE9PD1u2bIGfnx/Wr1+PLVu2yDwzsTavmYWFBQwMDCT6\nK3CJa8CAATh+/DiHyCTj6+tLZXIUVw4ePMj8397evmyaOpGBzMxM8tNPPzGP379/z/z/xIkTZN26\ndYQQQtLS0khwcDApKioimZmZJCAggJSWlspyCkIIISEhIQRAndtoxGVkZESsra3rXFwVtytXrpDY\n2Fipx48ePUr09fVZxdagQQOipKTE+/vVo0cPEhgYSO2aqaioECMjI+axlpYW0dXVpRbv2bNnyfLl\ny3m/LlVt0qh2kDIsLAzz589Heno6Jk+ejPj4eERGRmL69OkIDg7G/fv3MXbsWACAubk5XF1dMW3a\nNCxbtgwTJ07k5Q5Gy5YtERQUBGVlZeraANCmTRs0btyYqmZISIhCDNi6u7tXuShp7dq1rJd/9+jR\nA2pqapX2Ozg4oEmTJggMDKzSbKe2sLS0FPMp7dGjB7Zu3UptjsRPP/3Ea80ULq7W1XYxAgMDK+3r\n2rWr1Of7+PgwZq18oK+vj4kTJ+LkyZM4efIkL/Ub3r59y8s6BD4MXRSJffv2AShrvoaFhTH7zczM\nEBwcjJSUFCprNIYPH45FixZx1pFGYmIiJk2aBF1dXd6Xf1dHQECAWFL9+eef8c8//yA4OBiFhYXo\n1asXnJycxJL6qVOnsGbNGpn0FWqqtbq6Om7evMksejp58iTU1dWpG67QtmX39vbGw4cPmS/I/yJq\namqMD8a9e/eoO1B/jZWVFdLS0jjrqKqqQkVFBU+fPhX7oXz79i1Va31NTU1Wr+vduzfWr1+P/Px8\nODo64vbt27h27Ro0NTWxePFibN26FcOGDRO7k9iwYUOkpaXh8ePHOHHiRLXnUKip1gkJCWIrIo8c\nOYIZM2ZQPUdF23cajBw5ssa6F0OHDqXigSCNqlqP0tDV1cXOnTt5iEYyly5doqKzbNkyHDlyROKx\n5ORkaq3MxYsXs3pdRkYGDh48CCsrKzx8+BBDhgxB+/btmeP+/v6VphkMGDAAu3btkik5AAqWIBYs\nWCD2uLCwkPXFlYa6ujpVPaCsyXvmzBnqupJYsWIFb2MzQNmI+++//y7Xa/Lz83kx1JUGDQ8IOzs7\neHl5Ydq0aRKPb926Fe/fv+d8HoB9KYRbt25h+PDhzFL0EydOVFnMZ9GiRfj8+TO2b98u8zkUKkHE\nxcWJPS43f6VJTEwMNa2oqKhKg7RJSUnU9CXRunVr3Llzh5PG+/fvMXfuXInHOnToIHdFqaKiIurm\nM9LIzMyk0n1p2LAh9u/fX621PY3WSmJiImeN6pgzZw7OnDmDyMhIXLt2TebXKVSCuH79Ojp27Mg8\nrlevXi1GUzX+/v5o3bo187hp06Z49eoV710NrnU3YmJicPToUSxZsoTZZ2xsjHPnzkFbW5tKXQ8d\nHR0sWrSIaqGbcvT19alVDc/Pz0dpaWmVz6Hxfj5//lysZght/P394e/vz8r0RqESxIYNG8QSRF3m\n+vXrGDBgALNmpWnTpggLC6NW4IcmSkpKTFw+Pj6YMGGC2HFPT09kZWVV6zAuK7q6ujh16hRvRYx/\n+uknXnQrMmfOnErXqi4yd+5c7Ny5k1VyV6gEsXfv3kqrSusqt2/fFqvQ9ccff2DlypVVWrRzYdGi\nRawdqG/evImLFy9KPX7ixAlMnjyZWoJ49eqVXM3cusiCBQsqjYnVVaZOnYoff/yRVVFqhbrNWdOk\npqbCxsaGqiYfg6AAJDo7y4okzw59fX3s3bsX/fv3F6tjYWpqWiemBUti06ZN0NbWpqZnaGgIVVVV\nsUHPZs2a4ebNm8jMzIS5uTm1c/HBrl278M0336C0tBQLFy6UWkqwKhSqBVHT0E4OQM2XimNLx44d\nMWXKlEr7hw4dWgvRyMaJEyeoLRp8+fIlevfujZCQEIwZMwbA/5/PEhgYCGtra2RlZVE5F1+cO3cO\n9vb2sLa2Rnh4OCsNIUFUoEGDBnWyyjKfnD17tlJz+ezZsxJt53777beaCYoFEydOpDYI/PjxY4wY\nMQIXL15kuoovXrxAr1695LpNWJvs27ePs/mwQnYxlJWVQQipdoSZDZmZmQozzkGLXr161fg5VVRU\noKysTHUW7KBBg6hpAWVJ4uvbnGya6LLQunVrhISEUNMzMDBAVlYWxo4dy3n2rkK2IDp37qwwdzME\nJDN06FAq9TIFKrNlyxYEBwdTmdqvkC2IqkbcBRSDqKgoREVF1XYY/0lojhMpZAtCQECgZhAShICA\ngFSEBCEgICAVIUEICAhIRUgQAgICUlHIuxgCAlVhbm7O+Grm5OT8Tzp52djYwNPTE0DZ3J7Dhw+z\n0lHYFsTcuXPFSpxHRETUYjRV4+TkhNOnT1MryS5QNTExMWjVqhUeP378P1mE+PTp0zh8+DAaN26M\nx48fM/Z7rCYAyuxJXwNUZ0murKxMAJAxY8aQQYMGVTqemprKiyU4bXt5Wtbv5XGJRCKioqJC/e++\nevVqpX0ikYisWrWKtGzZskavmaxbYWEhOXXqFC9xKSsrExUVFaKiokJEIhEByizx5X0/VVRUSP/+\n/cmXL1+Y7dKlS5z+bjs7O0ZL2nOuX78u9Zg0FKoFUW5ismfPHhw9erSWo2GHnZ0dli9fTkXL0tIS\nAODs7IzHjx/DxMSEii4A2NraSnSmcnd3h6enJ3Jzc6vVaN68Oa8O55I4duwYdV9RLS0t+Pj4YN26\ndYiOjkZ0dDS6d+8OAHjz5o1ctvJubm6Ijo5Gq1atoKamxmwXLlxAly5doKKiAicnJ7ljfPDgAaMl\nDWdnZ7l1FWoMYtasWVKPhYaGVnm8rpCeno5Dhw5x1unVqxeGDh2KCRMm4Nq1axg9ejQ+fvxIIcKy\nxOPr6ytWCwIAwsPDUVhYiJEjR8rkGj1o0CDo6upStfGrih9//JGaDWGLFi0QHBwMACgoKEBycjKW\nL19eyfF87NixclXb0tLSwuDBgwGUlUHo2rUrXr58idLSUmhra6NevXqYNm0a/Pz8qPwd5QwcOBA3\nbtzAixcv5HthDfYgqoVt82/BggWkqKiIt2Yr1+Zyt27dSFZWFrG0tKQSj7W1NcnNzSUHDhxg9q1b\nt44YGxtT0X/37h1ZvHix2L4tW7aQkpISoqenJ/M109TU5KXrI2nz8/MjBQUFRFVVtdK1Wrhwodzv\nZXn1OFdXV2JgYFDp+OrVq0mDBg1kji8rK4upnpWVlUUiIyOJtrY2MTIyIvXr12fi0tXVJfn5+WTw\n4MHUrk379u3J/Pnzq3wvpKFQLQhJ6OrqQk1NjRfvBlosWbIEP/zwg/zZWwJt2rRBUlISZs2ahfr1\n6wMoqzfp7OyM4uJizvqvXr3CiRMnxFoPRkZGKC4uho6OjlzLh/Pz8znHIyv79u2TeLfiyZMnrFZK\nll9bSXTp0gVubm4oKSmRWU9XV5fxhExNTWWKKEmygcvLy5OpCycrbdu2BSGE1edDocYgJNG+fXu8\nffsWz58/r+1QpOLq6sr6NlNF3Nzc8OLFCzFfhosXL8LV1RX+/v6ctL29vbFp0yamlCJQVtRl27Zt\nSEhI4OwtwBdV1UaRVBmOCwsXLkRCQgJcXV3FClVXR35+Pv755x8AkFhmz8rKCpaWligsLMSkSZNw\n9uxZ1jFW7Bpu27aNtZbCdzH2799PrWktbaM1Iv/7779T0bG1tSUASHJyMrl16xb59ddfyYIFC6oc\nwZZl27p1K7l7967YvpSUFHLixAmZCvZyuWZhYWHMnQF5t9evX0vcf/z4cfLw4UNq7+XGjRtZd2Vf\nvnxJzMzMJB6zsbEhHz9+JJ06dSLa2trk1q1bpE+fPqzOs23bNlJcXExWrlxJAJCLFy+S1q1bExMT\nkypfJ406myA0NTWJubk5uXXrFjl58mSlP8jQ0JDk5+eT8+fPc/pS8PFh53t7/fo1+fjxI8nJyRHb\n//fff3PSXb58OXnx4gV58eIFycnJIYQQcufOnRq5Zl9Xz6b1njVu3Jjae6mtrU0OHTrEOh5lZWVi\nbm5OzM3NCQBy4sQJ5lirVq3I9evXiZqaGtHV1SX79u3j7bPTqFEjifulUWe7GF5eXti9ezc6d+6M\nfv36VTru4+ODHTt28OYSzRf169fH5MmT0ahRI9Yabdu2xbhx47B27Vqx/evXr8eQIUNY686aNQuW\nlpawtLTEqlWrcOTIEbHaHnwyZMgQTJ48mYrW5MmTERMTQ22SlK6uLrZv385pMp65uTnS0tKYCmOn\nTp1ijg0fPhwWFhawsLDAmDFjcO7cOc4xSyM5OVmu59fJQUpVVVV4eHhg7NixUgdrOPWrapGGDRui\ne/fuiI6OZq2RlZWFo0ePwsHBQWz/8uXLkZaWxnm8o2nTplBTU8PEiRM56cjD5s2bUVJSIleJvlGj\nRiEpKUmsvEBsbCwsLCwqVWHjQmxsLD5+/IgbN26w1sjKykKfPn2YL+jXf+fVq1ehp6eHJ0+e4OXL\nl2jWrBnnmCURFhYmd4X5ahNEdnY2NmzYgA8fPkAkEqF79+7o06cP8vLysG7dOrx58wYmJiaYNm0a\nU+kqJiYG8fHxUFZWxrhx4yTaqldFUVERpk6dyjzu2LEjkpKS4OLigr/++ksurbqEkpISoqOjeftV\nlmdUvSpSU1OxePFi5OTkUNGTFXlritrY2ODevXsAAJFIBEIIevbsSbXi2oMHD6CkpMSqaPHX5Ofn\nS01acXFxjIXihQsXEBMTg+XLl8PAwECs5IA0Hj9+LDWpKCkpwdnZGd26dWM1YFttglBWVsbYsWPR\nuHFjFBQUYObMmWjTpg3i4+Ph4OAAb29vHDt2DDExMfDz88PLly+RlJSEtWvXIjs7G4sXL8Zvv/1W\nqUalLLRr1w6WlpYYN24c/Pz8FDY5eHt7486dO5g5cyavTXYzMzOsWrWKs87atWupmqjyRbkTt7q6\nOlavXo0//viDanKwt7fH9evXMW7cOGqa1VFcXIxjx45h7dq1MiUHAExysLGxEavsfuPGDSxZsgSv\nX79mPYmw2gShr68PfX19AICGhgbMzMyQnZ2N5ORk5g3y8PDAggUL4Ofnh+TkZLi5uUFZWRkmJiYw\nNTVFamoqq2aTtrY2jIyMany6Llf09fWZL6q/vz+MjIygqalJrWZDVXD91V+1ahV+/vlnStHUDEpK\nSrh58yZV1+kWLVrAx8eH+m3S6vj8+TPrz7umpiaMjIyYx+rq6kxND7bINQaRlZWF58+fo3nz5vjw\n4QOTOPT19Zls9+7dOzRv3px5jYGBAd69e8cquMuXL/NS4JVvPn78yFTHJoTUWB2FlJQUFBQUiBXe\nlZeRI0cyU4wVhfz8fOrXeOXKlRgxYkSNz/3Q0dFhrPabN28u1/T5lJQUpKSkUI1H5gRRUFCANWvW\nYNy4cdDQ0Kh0XN4uxL1795j+IwAMGzYMHh4ecmnUFIoSV/ngJJfuwdatW6l0L9hes969e/O6LF6W\nuL799ltkZmbWaKL8Oq7yAcxp06bV2PkB4ODBg8z/7e3ty7orssxPKC4uJqGhoeT3339n9gUFBTHz\n1d+/f0+CgoIIIYTExMSQmJgY5nmhoaHk0aNHcs+DqEubENd/JzYhLskbp3kQmzZtgrm5udgy2vbt\n2zP1KS5evMgsUXVycsLVq1dRXFyMrKwsZGRk1Ol1EgICAtKptovx8OFDXL58GZaWlpgxYwZEIhF8\nfX0xcOBArF27FvHx8TA2NmaaQ+bm5nB1dcW0adOgoqKCiRMnsrqDISAgUPtUmyBatGghdVJPxUUh\n5fj4+CjcnQcBAYHK1Nmp1gJ1j68HsQT+NxASRA3y9OnT2g6BNfv37+e0zkNAMRESxFd88803+PTp\nE7777jte9M+fP8+L7tf069dPbB4KV1RVVdG2bVuMHDkSSkr0Py4aGhqV1pRwpV+/fujXrx+0tLQ4\nT5EGytbPTJo0iUJkikedXKxV0zRr1gzTp0/HvXv3MGHCBBw4cID6OUaMGMHZM3PYsGG4ePEisrKy\npD7HxcUFTk5OzCxXrkREREBJSYnzjLyKDBw4EF5eXvjy5QunhWsVCQ8Ph4eHB9atWwdlZWUYGxtz\n1gwICMC8efMoRFc1wcHBYnf8goKCOLtyrVmzBpmZmVixYgU7AZkmKNQQtXUv+M2bN2Tx4sVinn17\n9uyhdo+6a9euJDAwkLHtZ7utXbuW2NjYVBlXVlYWWbBgAZXrcvz4cWJqaiq3WUx11+zVq1dkzpw5\nxNTUVC5fx6r0Vq1aRQAQU1NTiR6SbN/LS5cuVWu2QiP+c+fOEUNDQ2JqakpMTU3JkSNHZPb/rLhp\namqSV69ekVevXpGSkhJSWFhIAgMDq3yNNIQuBgBjY2PMnz+f8ewzNjZGQEAANf34+HiEhYVRW21Z\nFfXq1cPr168563z77beYO3cu0tPTqazqrF+/PhwdHZGQkIDTp09j6dKlSE9PR2ZmJmftlJQUZtZj\nenq62NT+8iXWHTp0kFs3KCgIWlpaEltslpaW+OOPP9gHDcDCwgJnz56FmZkZrly5guzsbKSnpyM9\nPR2DBw+WebFWOQ0aNMBff/2Fy5cvY8+ePTAzM4OysjLU1dVhaGiIdu3ayR2j0MWogJ6eHnbu3Inw\n8HAqngK//PILFi1aRCEy2Xjw4AG2bt3KSeO7777Dli1bKEVUtmio3GylZ8+eKCwspKYNlJnNhISE\nIDo6Gg8fPgQAeHp6wsXFBdnZ2azqTADAunXrsG7dukr7bW1tMWLECAwcOJB1zOXT2SUN/LZq1Qoi\nkUiudRVGRkbYs2cP7t69i/Hjx7OOqyJCC6ICCQkJ6Nu3L8LCwqjoKcKy6a85deoUEhISqGoWFxdj\n2bJl8Pf3r5QcTp48yVm/sLAQKioqaNGiBYAy/5Ddu3fjzp077MrNVUNmZibCw8MlOlLLwl9//YXu\n3btj48aNEhdjOTg4yD1wm5eXhzlz5mDKlCmsYpKG0IIA0LlzZyQkJDDmN1/z+vVrXLt2rZYiE+fj\nx49VdlMyMjKQkZEBbW1tVh9ePT099OvXD6WlpVzCrERJSQn+/vtvicck2QnKS1FRETNpr2HDhvjz\nzz/RsGFDzrrllA92vnv3Dl++fOHc5WrUqBHMzMwkHtPR0YG7uzt+/PFHuTQLCgrw999/w8DAQOxv\nz8zMBCEEubm5KCoqkjtWoQWBsi9WRESEROerRo0a4fbt27UQVWVCQkLQoUMHTJw4kSmjpqysjIkT\nJ2LixInQ1NREmzZtMH36dLm1raysMGPGDOjo6IjtHz16NJXYra2tmTj79+9PRbMiPXr0kKnil7xY\nWVnh3Llz1NYUVUwOEydOhJmZGfr37w8fHx+5k8PXjB8/Hi9evMCiRYuwaNEixkBn1apVuHv3rtx6\nQgsCZZZd0mpKODk5sf5gzJ07l/o6lLdv3yI6OhpPnjzB2bNncezYMeZXeMyYMcjJycGjR4/k1m3b\nti3+/fffSgNj5cVeuFJQUMAMSJYPIq5btw5BQUFU9Hv27Indu3dDRYX+RzojIwO+vr64f/8+Fb2K\nRsutW7fGyZMnMWPGDCxdupST9urVq/HgwQMxU9zY2Fjs27eP3UzYmruJWT21veRV2sY2ruPHj3O+\ntSlpU1JSIiKRiISEhBCRSMQ8llYforpNWVmZHD16tMav2a1bt3i5LqdPn6b+Xn59rY4cOcJJgxBC\nTpw4wcQrEonIuXPniLu7O/XPCgCSlJQkU0ySELoY1cB2BLwcLy8viftdXFxYD3KVlpaCEAIAIIQw\nj5WUlFj5dpaUlGDQoEEAwFSt5pslS5ZQmXzl7u7O/P/r60ADOzu7SuUSCSGc3K2BMnOl/v37M/F6\neXlBSUmJtfNaVbx9+xaJiYmsX6/wCcLW1hY9evSQ+3XOzs5Vfvl//PFHbNq0SczjT142btwosW5H\nz549ceTIEV78DpctW8bp9Xv27MGcOXMoRcM/tO42SUJfXx+qqqpi+0pLSzlZ+kli69atePLkCbUu\nTDljx45FSEgIqzGpchRmDCIoKAjx8fGVBgyfPXtWqSS7LDRr1gzr1q2DlZUVPn36JHZszJgxyMrK\nQmhoKF69esUMCMrL6dOnYWlpWWngbN++fejYsSOruKvj6NGjnF7v5OTE+4QuJycnfPr0CQ8ePOCs\nJamFNnLkSM66AJCUlMS79f+1a9fg6OhIrWhQOf369UN4eDgsLS056ShMgpA0YQUouwfOZuJNZGQk\nIiMjAUCsyejo6Ig9e/awC1ICr169goWFBTU9vklPT+f9HMXFxdi+fTur224VqV+/PlOlrHyQVZ6i\nutVhZ2dHTetrdHV1sW3bNgwdOrTKtTVs0dTUxA8//MC526LwXQwaODo6Mpsis2bNmtoOQSaOHz8O\nW1tbKlrdunXDoEGDMGjQIE7lDGuazp07Q0tLSy7XankZMGAANDU1OWkoTAtCoHpWrlxZ2yHIhKen\nJ8s4IRAAABcdSURBVJXuBQC5SvXVJa5cuYK//vpL7vUWsqChoYGlS5fCxsYG/v7+nFaECglCoMah\nlRwUGT4SQzkFBQXU6nsKXQwBAQGpCAlCQEBAKkKCEBAQkIqQIAQEBKQiDFJKoEuXLhg2bBhVVykB\nga/52qeiLn/OhAQhgVatWuHPP/+s7TAE5GD48OHIycnBmTNn4O7ujubNm2PHjh21HVYldu3aBQ8P\nD3Tu3JnZZ2tri06dOtViVNIRuhgAtLS0sGPHDhBCEBcXh40bN2LPnj3U7dhtbW0ZS/pt27ZR0bx9\n+zZWr14NoGzW34gRI6g5WldHuYNTbWNkZIQWLVrgzJkzAMrmGNBODr6+vlR8QfLy8tC4cWOkpaUx\n2z///IPPnz+jXbt2cHBw4DzzViQSwcHBARs2bMDt27dRr169SkZIsvI/nyCmT5+OadOm4fXr11iy\nZInY3P5hw4ZRO0+vXr1w9epV9O7dG0CZSQhbDAwMmFWXbdq0wc8//wwA8Pb2RqtWrahbxrVq1Qpz\n5syBmpoa5syZAxcXFwBA3759WemZm5tjzpw5zCZtxausvH37FgsXLuSkIY3yGilRUVESDYXkRVp3\n4ubNm7h58ybGjBmD5cuXczqHpqYmDhw4gO3bt6NNmzZwcHCQ6ndSLbybPMgBm7X67du3J76+vqzX\nyjs5OZGWLVty9jaoanNxcSHp6enEx8eHyvr+2NhYYmlpWWP+GaNGjSKEEKKtrU2cnZ2Jqalpta+R\nFNuCBQtIUlISuXv3rtj7npGRQZKSkohIJOL9b5H3mj148KDSvi5dupABAwbwEpeuri558+YN6dWr\nF2ut69evk+joaOaxs7MzmTdvXpWvkYbCtCA0NTVhaGhYaf/Vq1ehr6/PWjc5OVlsma2SkhKmTJlC\n1Rfh2rVrMDU1RUxMDICyv4WL85GtrW0lnwKatGrVCrNnz2YeR0ZGQiQSIS8vD9evX5d7QVfjxo3x\n5s0bpKSkwNXVlXFtLt8aN25MZWZhRbs8LqiqqmLlypUS7fHq16+Ppk2b8lJpLDc3F3PnzuVkM1Cx\nmtj169cRGhrKSqvaT2l2djY2bNiADx8+QCQSoUePHvDy8sKhQ4dw/vx56OnpASjro7Vt2xYAEBMT\ng/j4eCgrK2PcuHFUmmbBwcFwcnLCgAEDmH2jR4/GvHnzqM7H9/f3x/v373krk6enp4cpU6bg0KFD\n+Oeff1hpcF3SXR13795l5V8ojeDg4CorXI0ZMwaenp4QiUSMEQ4bdu/ejeHDh1NZJeru7o6MjAyk\npqZWOvbixQusWLEC0dHR1Fe/6ujoYMuWLfDz82OtUVxcjIsXLwIo+15GRUWxD6i6Zv/79+/Jv//+\nSwghJD8/n0ydOpW8fPmSHDx4kJw4caLS89PS0khwcDApLi4mmZmZJCAggJSWlnLuYtjZ2ZH69esz\nj3/++WcydOhQKs07Ozs7cvjwYWJubk7y8vLIxIkTOTVLq9p0dHRIq1atqDeXf/jhB9aVmCpu5VWq\naMS2ePHiap/r7+9Pli5dyvmcLi4u1XZTZH0vw8PDSbNmzaQe37p1q0xdLXmvl66uLklISCAWFhZU\ndPv16yfT86RRbQtCX1+facJraGjAzMyMWWMuKdsnJyfDzc0NysrKMDExgampKVJTUzkvHhk0aBDO\nnDmD5ORkAGVOQuWVsLhy+/ZtqKqq4pdffkH9+vVRWlqK2NhYeHp6QkNDg8o5ysnNzaX661zOli1b\nqBi9/Pvvv3j//j2FiMrw8PCo9jnbtm3j1HIoh2Z5ghYtWuDx48fU9OSh/O4GV7Zt24YZM2Zw0pCr\nE5WVlYXnz58zX/bTp08jODgYmzdvxufPnwGUORZ/3X8yMDCg4rX37Nkz5ObmMo+Li4vRvXt39OzZ\nE1ZWVpy01dTUIBKJcP/+fRQVFaGkpATe3t7Q0NCAkZERdHV15dY0NzdHz549UVJSAkII7t+/jwYN\nGnCKsypKSkqoFB1u0qQJdV+MvLw8sVvGZ86cwYgRI5jHX3tsskFNTQ0tW7astJ/trc7u3bvLNAZF\no8RhRd6+fStXRS1pdOnSBRYWFpy7WzIniIKCAqxZswbjxo2DhoYGPD09sWHDBqxatQr6+vpUXZgk\nsW/fvkp99mbNmsHW1hb169fn7bzOzs7M3AV56NOnD86ePYugoCBMmTIFw4cPp1KHsiq+/tLRYNq0\naZw11q1bh4ULF+LQoUOMB6inpyfVCurLly+XWCjn22+/ZaVHa6m0vAQFBSEgIIDzbU6grEKcp6en\n2I8qG2QaSi8pKcHq1avRpUsXpgjq17+q3bt3Z8qLGxgYiFl+ZWdnw8DAoJLmvXv3cO/ePebxsGHD\nZGqO+vv7V6o96e3tDW9vb1n+FLlRVVWFnp6eXCX0goKCoKamhtjYWOZv/+2339C2bdtK1nnNmjWD\niooKK48EWa4XFyZMmMCq9QSIxyYSiRAXFwcfH59KcycmTJiA7du3cwkTEydOxIcPH8RmJ36No6Mj\n3rx5g7S0NJmvWXXvt6OjIyIiIqiVVvTw8EBycjLy8vJqrVzj13Uz7O3tYW9vX8XoxFesX7+e7Nq1\nq9LgZTknTpwg69atExukLCoqojpICYA0bdqUXLp0icrgjZ6eHrG3tydKSkrE3t6e2VRVVZn/lz9X\nnkHKw4cPkz/++EPisbi4OJKSkkJ++uknqgNbAIiZmRn1GhyRkZFUYqtqS0tL4xynPPVAZI1LS0uL\nGBoaSjw2btw4kpKSQoyNjWU+r6qqKgkNDSXff/89AcoGq+3t7Ul8fDxp1qwZyczMJCkpKcTOzo7q\neyjrxnqQ8uHDh7h8+TIsLS0xY8YMiEQi+Pr64sqVK3j27BlEIhGMjY2ZmVrm5uZwdXXFtGnToKKi\ngokTJ1KrLhUVFcXaYboi/fv3x969e6Gjo8MMGkZERGDGjBnMLSY29u+SqjWXw3XGYFUsWrQIs2fP\npmKA2qBBA9ja2mLUqFEUIpMO2y5ARdavX19pX/fu3XHjxg3WA64NGjTAqFGjsGHDBmYMbfjw4WjS\npAlevnwp9zR8LS0tKCkpMS7TXbp0QUBAAAYOHIgPHz5g48aNWLhwYY1Xg68WmX7aa4jqsnvr1q2p\nZUxjY2Pi5uZGlJSUiJubG3FzcyOampqcfnVqarOwsCABAQFicdnY2BBVVVVOujNmzCBXrlwhWlpa\nxNLSkpOWrJW1nJycqFyTfv36kStXrpArV66QtWvXkiZNmhANDQ1O72Xjxo3J0aNHSXR0NBkyZAgZ\nN24ccXV1pRJv/fr1ScOGDevMZ0waCpMgfvnlF0IIqZWLV9tvnqRt8uTJ5PPnzyQ7O5vs2rWr1uNh\nc810dXWpnnPPnj3VJsm6+F7WhbikoTDLve/cucP7nRJFYtOmTTAxMeFtkVJNkJCQwMy+pQGNUn4C\n4ihMgjh27BiOHTtW22EIUCQ4OLi2QxCoBoVZrCXw3+PcuXO1HYJANQgJQkBAQCpCghAQEJCKkCAE\nBASkojCDlDWFqqoq5s+fj6SkJMTFxVHVtrGxQWlpKT58+AATExMQQvDw4UOq56jLNGrUiPEPKaeu\nleEzNzeHjo4OPn/+jOfPn8Pa2hpqamp49+4d72tp6iJCgqhAo0aN0LdvX+zbt4+69tmzZ5Gfn4+d\nO3fC0dERAwYMgLa2NvXz1FW6dOmCdu3aie2bOXNmLUUjGQ8PDzg4OODJkyfYunUrBg4cCBMTEyQm\nJuL48eO1HZ5ceHl5oVWrVli1ahV7kRqaAyUTtT1ZBCjze7S2tuZlEouTkxNxdHQkZmZmRE9Pj7i5\nuZGuXbuS3r17s9JLSkoSW/dx7NgxkpCQQMaPH1/r17Em30sDAwOSkJBAOnfuzFtcgwcPJh06dFCo\n69W8eXPi7u4u03OlIbQg/g8NDQ1cunSJ2loPSZSb3ZRz9epVDBw4kJUpjbq6OgwMDODq6srsGzhw\nIKf4NDU1oaamBqDM7TswMBBv376Fra0tAFCxcuMDVVVVODg4SPyFf/z4MTp27Mj5HEeOHOGswSdK\nSkrQ0dFBXl4eYxz06NEjPHr0iJOuwieIr737YmNj0aJFi0pfRFkYOHAgBg0aRDO0alFWVsbbt29x\n5coVuV/7ww8/ICkpiWo8YWFhMDAwQH5+PoCyJc2pqano1KkTXF1dsW7dOmouXkCZZ8apU6c462Rm\nZkr1BNHR0YGjoyNu3LjB+Tx806tXLxgbG+PZs2dITEyU67W2trY4fPgwBg8eTHVcS+ETRGRkJPbu\n3YuTJ0/iwIEDePLkiVwJYufOnXj27FmtTFmOjo5GSUmJ3AnC1NQUo0ePxp07d6jGs3XrVty9excF\nBQVi+xMTExEQEAANDQ3k5eVROdeIESOoulBLY82aNQgPD6eiNXv2bCxbtoyKVjkbNmyAkZER7O3t\nkZWVhezsbFbWgdOmTcPIkSMlJgcbGxu0bt2aldmxwiWIvLw87N27F5MnTwZQtoz2y5cvKC4uxsmT\nJ+X+hRs8eDDrqkNciIuLQ+fOnSVa+VeHjo4Orly5QsXK72vYtLzY4uDggOjoaN7PM3XqVKZFxJU1\na9ZQ0fk6yTo6OuLly5eYM2cOtmzZgtLSUlaanTp1kloc5+nTp3j+/LnYvv37/1975x/S1PfG8fdl\nYjhFTdzEnwmKKKIJKlSWaSqhUBRJYEEmViw0U1ZQEBQlFmmWIShJlL++hgMNBbE/ylEWhT+yn4pY\nSyowpzZz2vx5Pn+Il9Rdndt198r3vOCC3p177ttn7tk5z7nnef6H1NTUVaeNG85BNDc3s84BAJsL\nc+nPplBTU4POzs5l35jriVQqRWFhIeRyuUUrGNPT07wkejUFNzc3aLVaXpLiAsCOHTug1+t5HwEt\nkJ6ejt+/f6Ouro435wAAk5OTZl13//59BAYG4u3bt8jMzDT6vk9PT2Nubg6Ojo4oKSlZc9r74OBg\n+Pj4wM/Pjz23f/9+hIaGIi4ubpnjOXLkCJqbm9lKb1xsuAelVkrIslYqKyuRkJBgUttLly7xcs8t\nW7YgICDAohR5Wq0WOp3OrNGHOSQkJODly5e8fdj27dvHW92RpXkzb968yToHPtm5cycbrF0r6enp\nSExMNEmTwWBAVVWVWfeRyWS4d+8ewsLCEBYWhrKyshWT767mHACssL4hANZaGnNyciK9vb0kKSnJ\n5CUoX19f0tjYuKb7lJaWLqvTYGtrS1xdXS3+G5ydncnHjx/Jhw8f1tVWvb29pLS0lDg4OPC2bNfa\n2sqLtrNnz5K/f/8u6m/pEvVadHEd3t7epKCggEil0nWzM1//+56enmZdx8WGG0EsoFKpLKpZUVdX\nx0bQ7ezsjCbW/Zdv376ZPD+MiorClStXoFAoQAjBrl270N3dje7ubkxNTS1K6msuOp0OKpUK4eHh\nKCoqsrg/LkZGRqBQKHgJTrq7u6O9vR07d+7kQdn8qoudnd2i/r58+QIAuH37Nvz9/Xm5DzBvh7VO\nYc3B3Izatra2kMlk+Pnzp0ntPT09TWq3IRyEm5sblEollEolG39oaGgwe8ltdHQUFy5cYH/Pzs5G\naGjoqteZMi04duwYYmNjceXKFfbcixcvEBQUhKCgICiVSkRHR5ul2xhTU1M4e/Ysb/0BgFwuh1Kp\nhLu7O2JiYnjLpRkTE8Obc1iNlpYW6HQ6i/tJS0vD9+/fkZeXZ/R1uVyOqKgoi++zQFtbm1nX+fn5\noba2Fl5eXqu2DQkJgUKhgEQiWbXthnAQ4+PjaGtrQ1tbG7q6ugDMxw/4WpNvampiaxkucOPGDajV\naqjVapPnnidOnIDBYGALpebl5S3rV6vVoqqqatl5IZFIJOzfqlar0dTUhNDQUOj1eszMzODr16+8\n3KempsYqAeGCggI0NDRYPFI7ffo0iouLjb527tw5qNVqFBUVmfytvZ709/eju7t7UZDSGC4uLlCp\nVOjo6DAp6LwhVjH0ej2eP3++bv2/e/cOV69eRXZ2Nvr7+xESErJohGFqnQIPDw+2/FtaWhoyMzOX\nbU6qqKhY19R5HR0d2L59O6ampky+ZiHz85kzZ1BeXr7sdXOLDC/g6ekJjUYDg8EABwcHttbGxMSE\n2ct6XExOTvK2yc7LywubNm2Cvb09xsfHUVdXh/j4eADztlqPuiTmFrqZmJhAT0/Pqh/6169fr60Q\nlPVCkKsjhr0Yxg6x6jKWrHZoaIgcOnRIcG3/2qy6uppkZGQQAKSsrIxUV1eT6upqizNnGzvKy8t5\nfS/z8/NJYWGhVe1l6ZGSkrLoMOUaLjbECILCzdJdp0ePHsWTJ08EUmOcf9f0T548yVu/ycnJOHjw\nIHQ6HTIyMgAAqampvPUPbNy8mdu2bYOtra3FI2/qIDYwGo0Gt27dWnSOr0ehNwINDQ1obm622gNj\nG4WamhqoVCoAsDhOtyGClBRu9Hr9ouP/iampKej1eoyPjwsthRfu3r3Ly8oLMO8Y+Aji0xEEhSIS\nsrKyLN6ezTd0BEGhiAiuZVWhYAidwFEoFA5ENYKora0VWoJRqK61I1ZtVNfaEJWDoFAo4oI6CAqF\nwomoHERwcLDQEoxCda0dsWqjutYGDVJSKBRORDWCoFAo4oI6CAqFwokonqTs6urCw4cPQQhBbGys\nxQVgLCEjIwNSqRQMw0AikeD69evQ6/W4c+cOtFot5HI5cnJyIJVK111LSUkJOjs74eTkhIKCAgBY\nUUt9fT1aWlogkUhw/PhxbN261Wq6VCoVnj59ym5vT0lJQVhYmFV1DQ8Po7i4GKOjo2AYBnFxcUhK\nShKFzZZqi4+PR2JioijstiJW2ce9ArOzsyQzM5MMDg6S6elpcu7cOfLjxw/B9GRkZJCxsbFF5yor\nK8njx48JIYTU19eTqqoqq2jp7u4mGo2GKJXKVbV8//6dnD9/nszMzJBfv36RzMxMMjc3ZzVdtbW1\npLGxcVlba+r6/fs30Wg0hBBC/v79S7KyssiPHz9EYTMubWKw20oIPsXo6+uDu7s7ZDIZbGxsEBUV\nZXbaLT4ghCzbHdje3o7du3cDmE+bZi19gYGBsLe3N0lLe3s7duzYAYlEArlcDnd3d/T19VlNFwCj\nuyqtqcvZ2Rm+vr4A5kspenp6Ynh4WBQ2M6Ztoa6J0HZbCcEdxMjIyKL07S4uLrwXhFkLDMMgNzcX\nFy9eZFOzj46OwtnZGcD8Gz06OiqYPi4tIyMjcHV1ZdsJYcfm5macP38epaWlbIJXoXQNDg6iv78f\nAQEBorPZgraFBLVisttSRBGDEBPXrl3D5s2b8efPH+Tm5sLDw2NZG4ZhBFBmHLFo2bt3L5KTk8Ew\nDB49eoSKigooFApBtBgMBhQWFuL48eNGM58LabOl2sRkN2MIPoJwcXFZlFx0ZGRk1RT068lCEVhH\nR0dERkair68Pzs7O7D59nU63LM+kNeHSstSOw8PDVrWjo6Mj+8GLi4tjh8PW1jU7O4tbt24hOjoa\nkZGRAMRjM2PaxGI3LgR3EP7+/hgYGIBWq8XMzAxevnyJiIgIQbRMTk6yWZcNBgPev38PHx8fhIeH\ns1mo1Wq1VfUtjYlwaYmIiMCrV68wMzODwcFBDAwM8FoXYjVd/yY6efPmDby9vQXRVVJSAi8vLyQl\nJbHnxGIzY9rEYjcuRPEkZVdXFx48eABCCPbs2SPYMufg4CDy8/PBMAxmZ2exa9cuHDhwAHq9Hrdv\n38bQ0BBkMhlycnKMBun4pqioCJ8/f8bY2BicnJxw+PBhREZGcmqpr6/Hs2fPYGNjs67LYsZ0ffr0\nCd++fQPDMJDJZDh16hQ777eWrp6eHly+fBk+Pj5gGAYMwyAlJQX+/v6C24xLW2trq+B2WwlROAgK\nhSJOBJ9iUCgU8UIdBIVC4YQ6CAqFwgl1EBQKhRPqICgUCifUQVAoFE6og6BQKJxQB0GhUDj5D8gK\nk3F27ryhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123e7d9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUkAAAFECAYAAACqFMx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1214a2400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cost = tf.reduce_mean(tf.reduce_mean(tf.squared_difference(X, Y), 1))\n",
    "learning_rate = 0.001\n",
    "\n",
    "# pass learning rate and cost to optimize\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)\n",
    "\n",
    "# Session to manage vars/train\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# Some parameters for training\n",
    "batch_size = 100\n",
    "n_epochs = 5\n",
    "\n",
    "# We'll try to reconstruct the same first 100 images and show how\n",
    "# The network does over the course of training.\n",
    "examples = ds.X[:100]\n",
    "\n",
    "# We'll store the reconstructions in a list\n",
    "imgs = []\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "for epoch_i in range(n_epochs):\n",
    "    for batch_X, _ in ds.train.next_batch():\n",
    "        sess.run(optimizer, feed_dict={X: batch_X - mean_img})\n",
    "    recon = sess.run(Y, feed_dict={X: examples - mean_img})\n",
    "    recon = np.clip((recon + mean_img).reshape((-1, 28, 28)), 0, 255)\n",
    "    img_i = montage(recon).astype(np.uint8)\n",
    "    imgs.append(img_i)\n",
    "    ax.imshow(img_i, cmap='gray')\n",
    "    fig.canvas.draw()\n",
    "    print(epoch_i, sess.run(cost, feed_dict={X: batch_X - mean_img}))\n",
    "gif.build_gif(imgs, saveto='conv-ae.gif', cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"conv-ae.gif?0.6145187909355793\" width=\"500\" height=\"500\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipyd.Image(url='conv-ae.gif?{}'.format(np.random.rand()),\n",
    "           height=500, width=500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"denoising-autoencoder\"></a>\n",
    "## Denoising Autoencoder\n",
    "\n",
    "The denoising autoencoder is a very simple extension to an autoencoder.  Instead of seeing the input, it is corrupted, for instance by masked noise.  but the reconstruction loss is still measured on the original uncorrupted image.  What this does is lets the model try to interpret occluded or missing parts of the thing it is reasoning about.  It would make sense for many models, that not every datapoint in an input is necessary to understand what is going on.  Denoising autoencoders try to enforce that, and as a result, the encodings at the middle most layer are often far more representative of the actual classes of different objects.\n",
    "\n",
    "In the resources section, you'll see that I've included a general framework autoencoder allowing you to use either a fully connected or convolutional autoencoder, and whether or not to include denoising.  If you interested in the mechanics of how this works, I encourage you to have a look at the code.\n",
    "\n",
    "<a name=\"variational-autoencoders\"></a>\n",
    "## Variational Autoencoders\n",
    "\n",
    "A variational autoencoder extends the traditional autoencoder by using an additional layer called the variational layer.  It is actually two networks that are cleverly connected using a simple reparameterization trick, to help the gradient flow through both networks during backpropagation allowing both to be optimized.\n",
    "\n",
    "We dont' have enough time to get into the details, but I'll try to quickly explain:  it tries to optimize the likelihood that a particular distribution would create an image, rather than trying to optimize simply the L2 loss at the end of the network.  Or put another way it hopes that there is some distribution that a distribution of image encodings could be defined as.  This is a bit tricky to grasp, so don't worry if you don't understand the details.  The major difference to hone in on is that instead of optimizing distance in the input space of pixel to pixel distance, which is actually quite arbitrary if you think about it... why would we care about the exact pixels being the same?  Human vision would not care for most cases, if there was a slight translation of our image, then the distance could be very high, but we would never be able to tell the difference.  So intuitively, measuring error based on raw pixel to pixel distance is not such a great approach.\n",
    "\n",
    "Instead of relying on raw pixel differences, the variational autoencoder tries to optimize two networks.  One which says that given my pixels, I am pretty sure I can encode them to the parameters of some well known distribution, like a set of Gaussians, instead of some artbitrary density of values.  And then I can optimize the latent space, by saying that particular distribution should be able to represent my entire dataset, and I try to optimize the likelihood that it will create the images I feed through a network.  So distance is somehow encoded in this latent space.  Of course I appreciate that is a difficult concept so forgive me for not being able to expand on it in more details.\n",
    "\n",
    "But to make up for the lack of time and explanation, I've included this model under the resources section for you to play with!  Just like the \"vanilla\" autoencoder, this one supports both fully connected, convolutional, and denoising models.\n",
    "\n",
    "This model performs so much better than the vanilla autoencoder.  In fact, it performs so well that I can even manage to encode the majority of MNIST into 2 values.  The following visualization demonstrates the learning of a variational autoencoder over time.\n",
    "\n",
    "<mnist visualization>\n",
    "\n",
    "There are of course a lot more interesting applications of such a model.  You could for instance, try encoding a more interesting dataset, such as CIFAR which you'll find a wrapper for in the libs/datasets module.\n",
    "\n",
    "<TODO: produce GIF visualization madness>\n",
    "\n",
    "Or the celeb faces dataset:\n",
    "\n",
    "<celeb dataset>\n",
    "\n",
    "Or you could try encoding an entire movie.  We tried it with the copyleft movie, \"Sita Sings The Blues\".  Every 2 seconds, we stored an image of this movie, and then fed all of these images to a deep variational autoencoder.  This is the result.\n",
    "\n",
    "<show sita sings the blues training images>\n",
    "\n",
    "And I'm sure we can get closer with deeper nets and more train time.  But notice how in both celeb faces and sita sings the blues, the decoding is really blurred.  That is because of the assumption of the underlying representational space.  We're saying the latent space must be modeled as a gaussian, and those factors must be distributed as a gaussian.  This enforces a sort of discretization of my representation, enforced by the noise parameter of the gaussian.  In the last session, we'll see how we can avoid this sort of blurred representation and get even better decodings using a generative adversarial network.\n",
    "\n",
    "For now, consider the applications that this method opens up.  Once you have an encoding of a movie, or image dataset, you are able to do some very interesting things.  You have effectively stored all the representations of that movie, although its not perfect of course.  But, you could for instance, see how another movie would be interpretted by the same network.  That's similar to what Terrance Broad did for his project on reconstructing blade runner and a scanner darkly, though he made use of both the variational autoencoder and the generative adversarial network.  We're going to look at that network in more detail in the last session.\n",
    "\n",
    "We'll also look at how to properly handle very large datasets like celeb faces or the one used here to create the sita sings the blues autoencoder.  Taking every 60th frame of Sita Sings The Blues gives you aobut 300k images.  And that's a lot of data to try and load in all at once.  We had to size it down considerably, and make use of what's called a tensorflow input pipeline.  I've included all the code for training this network, which took about 1 day on a fairly powerful machine, but I will not get into the details of the image pipeline bits until session 5 when we look at generative adversarial networks.  I'm delaying this because we'll need to learn a few things along the way before we can build such a network.\n",
    "\n",
    "<a name=\"predicting-image-labels\"></a>\n",
    "# Predicting Image Labels\n",
    "\n",
    "We've just seen a variety of types of autoencoders and how they are capable of compressing information down to its inner most layer while still being able to retain most of the interesting details.  Considering that the CelebNet dataset was nearly 200 thousand images of 64 x 64 x 3 pixels, and we're able to express those with just an inner layer of 50 values, that's just magic basically.  Magic.\n",
    "\n",
    "Okay, let's move on now to a different type of learning often called supervised learning.  Unlike what we just did, which is work with a set of data and not have any idea what that data should be *labeled* as, we're going to explicitly tell the network what we want it to be labeled by saying what the network should output for a given input.  In the previous cause, we just had a set of `Xs`, our images.  Now, we're going to have `Xs` and `Ys` given to us, and use the `Xs` to try and output the `Ys`.\n",
    "\n",
    "With MNIST, the outputs of each image are simply what numbers are drawn in the input image.  The wrapper for grabbing this dataset from the libs module takes an additional parameter which I didn't talk about called `one_hot`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from libs import datasets\n",
    "# ds = datasets.MNIST(one_hot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To see what this is doing, let's compare setting it to false versus true:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "6\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x120a57c88>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sn8BoYUQwIdCuAacJ2TWYnZ31NALz8/OeFcDLZRHo38NxAnYNpO24ZATq9bpvUKlYBBwj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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123ed1358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = datasets.MNIST(one_hot=False)\n",
    "# let's look at the first label\n",
    "print(ds.Y[0])\n",
    "# okay and what does the input look like\n",
    "plt.imshow(np.reshape(ds.X[0], (28, 28)), cmap='gray')\n",
    "# great it is just the label of the image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x120b49ba8>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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QKJh6A1QngLvlKFORqxnARSKQoctchJeNU/g9if3SpcirE0kbQiqVQrlcNm5LWVm5VqsB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\nfoRgSziiufQk8NeICGQKMM3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x120a756a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "# Let's look at the next one just to be sure\n",
    "print(ds.Y[1])\n",
    "# Yea the same idea\n",
    "plt.imshow(np.reshape(ds.X[1], (28, 28)), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let's look at what the one hot version looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n",
      "[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n",
      "[ 0.  0.  1.  0.  0.  0.  0.  0.  0.  0.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ki4CLgN/gEupFmMvlsLS0pCwC3qmYYgciBMODCMEWR98q5P0IKR6guwa8ylCPC5RKJRQK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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1207cf080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = datasets.MNIST(one_hot=True)\n",
    "plt.figure()\n",
    "plt.imshow(np.reshape(ds.X[0], (28, 28)), cmap='gray')\n",
    "print(ds.Y[0])\n",
    "# array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.,  0.])\n",
    "# Woah a bunch more numbers.  10 to be exact, which is also the number\n",
    "# of different labels in the dataset.\n",
    "plt.imshow(np.reshape(ds.X[1], (28, 28)), cmap='gray')\n",
    "print(ds.Y[1])\n",
    "# array([ 0.,  0.,  0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So instead of have a number from 0-9, we have 10 numbers corresponding to the digits, 0-9, and each value is either 0 or 1.  Whichever digit the image represents is the one that is 1.\n",
    "\n",
    "To summarize, we have all of the images of the dataset stored as:\n",
    "`n_observations` x `n_features` tensor (n-dim array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 784)\n"
     ]
    }
   ],
   "source": [
    "print(ds.X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And labels stored as `n_observations` x `n_labels` where each observation is a one-hot vector, where only one element is 1 indicating which class or label it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70000, 10)\n",
      "[ 0.  0.  0.  0.  0.  0.  0.  1.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "print(ds.Y.shape)\n",
    "print(ds.Y[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"one-hot-encoding\"></a>\n",
    "## One-Hot Encoding\n",
    "\n",
    "Remember in the last session, we saw how to build a network capable of taking 2 inputs representing the row and column of an image, and predicting 3 outputs, the red, green, and blue colors.  Just like in our unsupervised model, instead of having 2 inputs, we'll now have 784 inputs, the brightness of every pixel in our image.  And instead of 3 outputs, like in our painting network from last session, or the 784 outputs we had in our unsupervised MNIST network, we'll now have 10 outputs representing the one-hot encoding of its label.\n",
    "\n",
    "So why don't we just have 1 output?  A number from 0-9?  Wouldn't having 10 different outputs instead of just 1 be harder to learn?  Consider how we normally train the network.  We have to give it a cost which it will use to minimize.  What could our cost be if our output was just a single number, 0-9?  We would still have the true label, and the predicted label.  Could we just take the subtraction of the two values?  e.g. the network predicted 0, but the image was really the number 8.  Okay so then our distance could be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# cost = tf.reduce_sum(tf.abs(y_pred - y_true))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But in this example, the cost would be 8.  If the image was a 4, and the network predicted a 0 again, the cost would be 4... but isn't the network still just as wrong, not half as much as when the image was an 8?  In a one-hot encoding, the cost would be 1 for both, meaning they are both just as wrong.  So we're able to better measure the cost, by separating each class's label into its own dimension.\n",
    "\n",
    "<a name=\"using-regression-for-classification\"></a>\n",
    "## Using Regression for Classification\n",
    "\n",
    "The network we build will be trained to output values between 0 and 1.  They won't output exactly a 0 or 1.  But rather, they are able to produce any value.  0, 0.1, 0.2, ...  and that means the networks we've been using are actually performing regression.  In regression, the output is \"continuous\", rather than \"discrete\".  The difference is this: a *discrete* output means the network can only output one of a few things.  Like, 0, 1, 2, or 3, and that's it.  But a *continuous* output means it can output any real number.\n",
    "\n",
    "In order to perform what's called classification, we're just simply going to look at whichever value is the highest in our one hot encoding.  In order to do that a little better, we're actually going interpret our one hot encodings as probabilities by scaling the total output by their sum.  What this does is allows us to understand that as we grow more confident in one prediction, we should grow less confident in all other predictions. We only have so much certainty to go around, enough to add up to 1.  If we think the image might also be the number 1, then we lose some certainty of it being the number 0.\n",
    "\n",
    "It turns out there is a better cost function that simply measuring the distance between two vectors when they are probabilities.  It's called cross entropy:\n",
    "\n",
    "\\begin{align}\n",
    "\\Large{H(x) = -\\sum{y_{\\text{t}}(x) * \\log(y_{\\text{p}}(x))}}\n",
    "\\end{align}\n",
    "\n",
    "What this equation does is measures the similarity of our prediction with our true distribution, by exponentially increasing error whenever our prediction gets closer to 1 when it should be 0, and similarly by exponentially increasing error whenever our prediction gets closer to 0, when it should be 1.   I won't go into more detail here, but just know that we'll be using this measure instead of a normal distance measure.\n",
    "\n",
    "<a name=\"fully-connected-network\"></a>\n",
    "## Fully Connected Network\n",
    "\n",
    "### Defining the Network\n",
    "\n",
    "Let's see how our one hot encoding and our new cost function will come into play.  We'll create our network for predicting image classes in pretty much the same way we've created previous networks:\n",
    "\n",
    "We will have as input to the network 28 x 28 values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from libs import datasets\n",
    "ds = datasets.MNIST(split=[0.8, 0.1, 0.1])\n",
    "n_input = 28 * 28"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As output, we have our 10 one-hot-encoding values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_output = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to create placeholders for our tensorflow graph.  We're going to set the first dimension to `None`.  Remember from our unsupervised model, this is just something special for placeholders which tells tensorflow \"let this dimension be any possible value\".  1, 5, 100, 1000, it doesn't matter.  Since we're going to pass our entire dataset in batches we'll need this to be say 100 images at a time.  But we'd also like to be able to send in only 1 image and see what the prediction of the network is.  That's why we let this dimension be flexible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = tf.placeholder(tf.float32, [None, n_input])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the output, we'll have `None` again, since for every input, we'll have the same number of images that have outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Y = tf.placeholder(tf.float32, [None, n_output])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll connect our input to the output with a linear layer.  Instead of `relu`, we're going to use `softmax`.  This will perform our exponential scaling of the outputs and make sure the output sums to 1, making it a probability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We'll use the linear layer we created in the last session, which I've stored in the libs file:\n",
    "# NOTE: The lecture used an older version of this function which had a slightly different definition.\n",
    "from libs import utils\n",
    "Y_pred, W = utils.linear(\n",
    "    x=X,\n",
    "    n_output=n_output,\n",
    "    activation=tf.nn.softmax,\n",
    "    name='layer1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we write our loss function as the cross entropy. And then we'll give our optimizer the `cross_entropy` measure just like we would with GradientDescent.  The formula for cross entropy is:\n",
    "\n",
    "\\begin{align}\n",
    "\\Large{H(x) = -\\sum{\\text{Y}_{\\text{true}} * log(\\text{Y}_{pred})}}\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We add 1e-12 because the log is undefined at 0.\n",
    "cross_entropy = -tf.reduce_sum(Y * tf.log(Y_pred + 1e-12))\n",
    "optimizer = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To determine the correct class from our regression output, we have to take the maximum index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predicted_y = tf.argmax(Y_pred, 1)\n",
    "actual_y = tf.argmax(Y, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then measure the accuracy by seeing whenever these are equal.  Note, this is just for us to see, and is not at all used to \"train\" the network!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(predicted_y, actual_y)\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the Network\n",
    "\n",
    "The rest of the code will be exactly the same as before.  We chunk the training dataset into `batch_size` chunks, and let these images help train the network over a number of iterations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.891\n",
      "0.905571\n",
      "0.907714\n",
      "0.915571\n",
      "0.913286\n",
      "0.922143\n"
     ]
    }
   ],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "# Now actually do some training:\n",
    "batch_size = 50\n",
    "n_epochs = 5\n",
    "for epoch_i in range(n_epochs):\n",
    "    for batch_xs, batch_ys in ds.train.next_batch():\n",
    "        sess.run(optimizer, feed_dict={\n",
    "            X: batch_xs,\n",
    "            Y: batch_ys\n",
    "        })\n",
    "    valid = ds.valid\n",
    "    print(sess.run(accuracy,\n",
    "                   feed_dict={\n",
    "                       X: valid.images,\n",
    "                       Y: valid.labels\n",
    "                   }))\n",
    "\n",
    "# Print final test accuracy:\n",
    "test = ds.test\n",
    "print(sess.run(accuracy,\n",
    "               feed_dict={\n",
    "                   X: test.images,\n",
    "                   Y: test.labels\n",
    "               }))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we should see is the accuracy being printed after each \"epoch\", or after every run over the entire dataset.  Since we're using batches, we use the notion of an \"epoch\" to denote whenever we've gone through the entire dataset.\n",
    "\n",
    "<a name=\"inspecting-the-network\"></a>\n",
    "### Inspecting the Trained Network\n",
    "\n",
    "Let's try and now inspect *how* the network is accomplishing this task.  We know that our network is a single matrix multiplication of our 784 pixel values.  The weight matrix, `W`, should therefore have 784 rows.  As outputs, it has 10 values.  So the matrix is composed in the `linear` function as `n_input` x `n_output` values.  So the matrix is 784 rows x 10 columns.\n",
    "\n",
    "<TODO: graphic w/ wacom showing network and matrix multiplication and pulling out single neuron/column>\n",
    "\n",
    "In order to get this matrix, we could have had our `linear` function return the `tf.Tensor`.  But since everything is part of the tensorflow graph, and we've started using nice names for all of our operations, we can actually find this tensor using tensorflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Placeholder',\n",
       " 'Reshape/shape',\n",
       " 'Reshape',\n",
       " 'encoder/layer/0/W',\n",
       " 'encoder/layer/0/W/Initializer/random_normal/shape',\n",
       " 'encoder/layer/0/W/Initializer/random_normal/mean',\n",
       " 'encoder/layer/0/W/Initializer/random_normal/stddev',\n",
       " 'encoder/layer/0/W/Initializer/random_normal/RandomStandardNormal',\n",
       " 'encoder/layer/0/W/Initializer/random_normal/mul',\n",
       " 'encoder/layer/0/W/Initializer/random_normal',\n",
       " 'encoder/layer/0/W/Assign',\n",
       " 'encoder/layer/0/W/read',\n",
       " 'encoder/layer/0/Conv2D',\n",
       " 'encoder/layer/0/Relu',\n",
       " 'encoder/layer/1/W',\n",
       " 'encoder/layer/1/W/Initializer/random_normal/shape',\n",
       " 'encoder/layer/1/W/Initializer/random_normal/mean',\n",
       " 'encoder/layer/1/W/Initializer/random_normal/stddev',\n",
       " 'encoder/layer/1/W/Initializer/random_normal/RandomStandardNormal',\n",
       " 'encoder/layer/1/W/Initializer/random_normal/mul',\n",
       " 'encoder/layer/1/W/Initializer/random_normal',\n",
       " 'encoder/layer/1/W/Assign',\n",
       " 'encoder/layer/1/W/read',\n",
       " 'encoder/layer/1/Conv2D',\n",
       " 'encoder/layer/1/Relu',\n",
       " 'encoder/layer/2/W',\n",
       " 'encoder/layer/2/W/Initializer/random_normal/shape',\n",
       " 'encoder/layer/2/W/Initializer/random_normal/mean',\n",
       " 'encoder/layer/2/W/Initializer/random_normal/stddev',\n",
       " 'encoder/layer/2/W/Initializer/random_normal/RandomStandardNormal',\n",
       " 'encoder/layer/2/W/Initializer/random_normal/mul',\n",
       " 'encoder/layer/2/W/Initializer/random_normal',\n",
       " 'encoder/layer/2/W/Assign',\n",
       " 'encoder/layer/2/W/read',\n",
       " 'encoder/layer/2/Conv2D',\n",
       " 'encoder/layer/2/Relu',\n",
       " 'decoder/layer/0/Shape',\n",
       " 'decoder/layer/0/Slice/begin',\n",
       " 'decoder/layer/0/Slice/size',\n",
       " 'decoder/layer/0/Slice',\n",
       " 'decoder/layer/0/Squeeze',\n",
       " 'decoder/layer/0/pack/1',\n",
       " 'decoder/layer/0/pack/2',\n",
       " 'decoder/layer/0/pack/3',\n",
       " 'decoder/layer/0/pack',\n",
       " 'decoder/layer/0/conv2d_transpose',\n",
       " 'decoder/layer/0/Relu',\n",
       " 'decoder/layer/1/Shape',\n",
       " 'decoder/layer/1/Slice/begin',\n",
       " 'decoder/layer/1/Slice/size',\n",
       " 'decoder/layer/1/Slice',\n",
       " 'decoder/layer/1/Squeeze',\n",
       " 'decoder/layer/1/pack/1',\n",
       " 'decoder/layer/1/pack/2',\n",
       " 'decoder/layer/1/pack/3',\n",
       " 'decoder/layer/1/pack',\n",
       " 'decoder/layer/1/conv2d_transpose',\n",
       " 'decoder/layer/1/Relu',\n",
       " 'decoder/layer/2/Shape',\n",
       " 'decoder/layer/2/Slice/begin',\n",
       " 'decoder/layer/2/Slice/size',\n",
       " 'decoder/layer/2/Slice',\n",
       " 'decoder/layer/2/Squeeze',\n",
       " 'decoder/layer/2/pack/1',\n",
       " 'decoder/layer/2/pack/2',\n",
       " 'decoder/layer/2/pack/3',\n",
       " 'decoder/layer/2/pack',\n",
       " 'decoder/layer/2/conv2d_transpose',\n",
       " 'decoder/layer/2/Relu',\n",
       " 'Reshape_1/shape',\n",
       " 'Reshape_1',\n",
       " 'SquaredDifference',\n",
       " 'Mean/axis',\n",
       " 'Mean',\n",
       " 'Rank',\n",
       " 'range/start',\n",
       " 'range/delta',\n",
       " 'range',\n",
       " 'Mean_1',\n",
       " 'gradients/Shape',\n",
       " 'gradients/Const',\n",
       " 'gradients/Fill',\n",
       " 'gradients/Mean_1_grad/Shape',\n",
       " 'gradients/Mean_1_grad/Size',\n",
       " 'gradients/Mean_1_grad/add',\n",
       " 'gradients/Mean_1_grad/mod',\n",
       " 'gradients/Mean_1_grad/Shape_1',\n",
       " 'gradients/Mean_1_grad/range/start',\n",
       " 'gradients/Mean_1_grad/range/delta',\n",
       " 'gradients/Mean_1_grad/range',\n",
       " 'gradients/Mean_1_grad/Fill/value',\n",
       " 'gradients/Mean_1_grad/Fill',\n",
       " 'gradients/Mean_1_grad/DynamicStitch',\n",
       " 'gradients/Mean_1_grad/Maximum/y',\n",
       " 'gradients/Mean_1_grad/Maximum',\n",
       " 'gradients/Mean_1_grad/floordiv',\n",
       " 'gradients/Mean_1_grad/Reshape',\n",
       " 'gradients/Mean_1_grad/Tile',\n",
       " 'gradients/Mean_1_grad/Shape_2',\n",
       " 'gradients/Mean_1_grad/Shape_3',\n",
       " 'gradients/Mean_1_grad/Rank',\n",
       " 'gradients/Mean_1_grad/range_1/start',\n",
       " 'gradients/Mean_1_grad/range_1/delta',\n",
       " 'gradients/Mean_1_grad/range_1',\n",
       " 'gradients/Mean_1_grad/Prod',\n",
       " 'gradients/Mean_1_grad/Rank_1',\n",
       " 'gradients/Mean_1_grad/range_2/start',\n",
       " 'gradients/Mean_1_grad/range_2/delta',\n",
       " 'gradients/Mean_1_grad/range_2',\n",
       " 'gradients/Mean_1_grad/Prod_1',\n",
       " 'gradients/Mean_1_grad/Maximum_1/y',\n",
       " 'gradients/Mean_1_grad/Maximum_1',\n",
       " 'gradients/Mean_1_grad/floordiv_1',\n",
       " 'gradients/Mean_1_grad/Cast',\n",
       " 'gradients/Mean_1_grad/truediv',\n",
       " 'gradients/Mean_grad/Shape',\n",
       " 'gradients/Mean_grad/Size',\n",
       " 'gradients/Mean_grad/add',\n",
       " 'gradients/Mean_grad/mod',\n",
       " 'gradients/Mean_grad/Shape_1',\n",
       " 'gradients/Mean_grad/range/start',\n",
       " 'gradients/Mean_grad/range/delta',\n",
       " 'gradients/Mean_grad/range',\n",
       " 'gradients/Mean_grad/Fill/value',\n",
       " 'gradients/Mean_grad/Fill',\n",
       " 'gradients/Mean_grad/DynamicStitch',\n",
       " 'gradients/Mean_grad/Maximum/y',\n",
       " 'gradients/Mean_grad/Maximum',\n",
       " 'gradients/Mean_grad/floordiv',\n",
       " 'gradients/Mean_grad/Reshape',\n",
       " 'gradients/Mean_grad/Tile',\n",
       " 'gradients/Mean_grad/Shape_2',\n",
       " 'gradients/Mean_grad/Shape_3',\n",
       " 'gradients/Mean_grad/Rank',\n",
       " 'gradients/Mean_grad/range_1/start',\n",
       " 'gradients/Mean_grad/range_1/delta',\n",
       " 'gradients/Mean_grad/range_1',\n",
       " 'gradients/Mean_grad/Prod',\n",
       " 'gradients/Mean_grad/Rank_1',\n",
       " 'gradients/Mean_grad/range_2/start',\n",
       " 'gradients/Mean_grad/range_2/delta',\n",
       " 'gradients/Mean_grad/range_2',\n",
       " 'gradients/Mean_grad/Prod_1',\n",
       " 'gradients/Mean_grad/Maximum_1/y',\n",
       " 'gradients/Mean_grad/Maximum_1',\n",
       " 'gradients/Mean_grad/floordiv_1',\n",
       " 'gradients/Mean_grad/Cast',\n",
       " 'gradients/Mean_grad/truediv',\n",
       " 'gradients/SquaredDifference_grad/Shape',\n",
       " 'gradients/SquaredDifference_grad/Shape_1',\n",
       " 'gradients/SquaredDifference_grad/BroadcastGradientArgs',\n",
       " 'gradients/SquaredDifference_grad/scalar',\n",
       " 'gradients/SquaredDifference_grad/mul',\n",
       " 'gradients/SquaredDifference_grad/sub',\n",
       " 'gradients/SquaredDifference_grad/mul_1',\n",
       " 'gradients/SquaredDifference_grad/Sum',\n",
       " 'gradients/SquaredDifference_grad/Reshape',\n",
       " 'gradients/SquaredDifference_grad/Sum_1',\n",
       " 'gradients/SquaredDifference_grad/Reshape_1',\n",
       " 'gradients/SquaredDifference_grad/Neg',\n",
       " 'gradients/SquaredDifference_grad/tuple/group_deps',\n",
       " 'gradients/SquaredDifference_grad/tuple/control_dependency',\n",
       " 'gradients/SquaredDifference_grad/tuple/control_dependency_1',\n",
       " 'gradients/Reshape_1_grad/Shape',\n",
       " 'gradients/Reshape_1_grad/Reshape',\n",
       " 'gradients/decoder/layer/2/Relu_grad/ReluGrad',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/Shape',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/Conv2DBackpropFilter',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/Conv2D',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/tuple/group_deps',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/tuple/control_dependency',\n",
       " 'gradients/decoder/layer/2/conv2d_transpose_grad/tuple/control_dependency_1',\n",
       " 'gradients/decoder/layer/1/Relu_grad/ReluGrad',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/Shape',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/Conv2DBackpropFilter',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/Conv2D',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/tuple/group_deps',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/tuple/control_dependency',\n",
       " 'gradients/decoder/layer/1/conv2d_transpose_grad/tuple/control_dependency_1',\n",
       " 'gradients/decoder/layer/0/Relu_grad/ReluGrad',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/Shape',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/Conv2DBackpropFilter',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/Conv2D',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/tuple/group_deps',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/tuple/control_dependency',\n",
       " 'gradients/decoder/layer/0/conv2d_transpose_grad/tuple/control_dependency_1',\n",
       " 'gradients/encoder/layer/2/Relu_grad/ReluGrad',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/Shape',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/Conv2DBackpropInput',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/Shape_1',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/Conv2DBackpropFilter',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/tuple/group_deps',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/tuple/control_dependency',\n",
       " 'gradients/encoder/layer/2/Conv2D_grad/tuple/control_dependency_1',\n",
       " 'gradients/encoder/layer/1/Relu_grad/ReluGrad',\n",
       " 'gradients/AddN',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/Shape',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/Conv2DBackpropInput',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/Shape_1',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/Conv2DBackpropFilter',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/tuple/group_deps',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/tuple/control_dependency',\n",
       " 'gradients/encoder/layer/1/Conv2D_grad/tuple/control_dependency_1',\n",
       " 'gradients/encoder/layer/0/Relu_grad/ReluGrad',\n",
       " 'gradients/AddN_1',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/Shape',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/Conv2DBackpropInput',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/Shape_1',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/Conv2DBackpropFilter',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/tuple/group_deps',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/tuple/control_dependency',\n",
       " 'gradients/encoder/layer/0/Conv2D_grad/tuple/control_dependency_1',\n",
       " 'gradients/AddN_2',\n",
       " 'beta1_power/initial_value',\n",
       " 'beta1_power',\n",
       " 'beta1_power/Assign',\n",
       " 'beta1_power/read',\n",
       " 'beta2_power/initial_value',\n",
       " 'beta2_power',\n",
       " 'beta2_power/Assign',\n",
       " 'beta2_power/read',\n",
       " 'zeros',\n",
       " 'encoder/layer/0/W/Adam',\n",
       " 'encoder/layer/0/W/Adam/Assign',\n",
       " 'encoder/layer/0/W/Adam/read',\n",
       " 'zeros_1',\n",
       " 'encoder/layer/0/W/Adam_1',\n",
       " 'encoder/layer/0/W/Adam_1/Assign',\n",
       " 'encoder/layer/0/W/Adam_1/read',\n",
       " 'zeros_2',\n",
       " 'encoder/layer/1/W/Adam',\n",
       " 'encoder/layer/1/W/Adam/Assign',\n",
       " 'encoder/layer/1/W/Adam/read',\n",
       " 'zeros_3',\n",
       " 'encoder/layer/1/W/Adam_1',\n",
       " 'encoder/layer/1/W/Adam_1/Assign',\n",
       " 'encoder/layer/1/W/Adam_1/read',\n",
       " 'zeros_4',\n",
       " 'encoder/layer/2/W/Adam',\n",
       " 'encoder/layer/2/W/Adam/Assign',\n",
       " 'encoder/layer/2/W/Adam/read',\n",
       " 'zeros_5',\n",
       " 'encoder/layer/2/W/Adam_1',\n",
       " 'encoder/layer/2/W/Adam_1/Assign',\n",
       " 'encoder/layer/2/W/Adam_1/read',\n",
       " 'Adam/learning_rate',\n",
       " 'Adam/beta1',\n",
       " 'Adam/beta2',\n",
       " 'Adam/epsilon',\n",
       " 'Adam/update_encoder/layer/0/W/ApplyAdam',\n",
       " 'Adam/update_encoder/layer/1/W/ApplyAdam',\n",
       " 'Adam/update_encoder/layer/2/W/ApplyAdam',\n",
       " 'Adam/mul',\n",
       " 'Adam/Assign',\n",
       " 'Adam/mul_1',\n",
       " 'Adam/Assign_1',\n",
       " 'Adam',\n",
       " 'init',\n",
       " 'Placeholder_1',\n",
       " 'Placeholder_2',\n",
       " 'layer1/W',\n",
       " 'layer1/W/Initializer/random_uniform/shape',\n",
       " 'layer1/W/Initializer/random_uniform/min',\n",
       " 'layer1/W/Initializer/random_uniform/max',\n",
       " 'layer1/W/Initializer/random_uniform/RandomUniform',\n",
       " 'layer1/W/Initializer/random_uniform/sub',\n",
       " 'layer1/W/Initializer/random_uniform/mul',\n",
       " 'layer1/W/Initializer/random_uniform',\n",
       " 'layer1/W/Assign',\n",
       " 'layer1/W/read',\n",
       " 'layer1/b',\n",
       " 'layer1/b/Initializer/Const',\n",
       " 'layer1/b/Assign',\n",
       " 'layer1/b/read',\n",
       " 'layer1/MatMul',\n",
       " 'layer1/h',\n",
       " 'layer1/Softmax',\n",
       " 'add/y',\n",
       " 'add',\n",
       " 'Log',\n",
       " 'mul',\n",
       " 'Rank_1',\n",
       " 'range_1/start',\n",
       " 'range_1/delta',\n",
       " 'range_1',\n",
       " 'Sum',\n",
       " 'Neg',\n",
       " 'gradients_1/Shape',\n",
       " 'gradients_1/Const',\n",
       " 'gradients_1/Fill',\n",
       " 'gradients_1/Neg_grad/Neg',\n",
       " 'gradients_1/Sum_grad/Shape',\n",
       " 'gradients_1/Sum_grad/Size',\n",
       " 'gradients_1/Sum_grad/add',\n",
       " 'gradients_1/Sum_grad/mod',\n",
       " 'gradients_1/Sum_grad/Shape_1',\n",
       " 'gradients_1/Sum_grad/range/start',\n",
       " 'gradients_1/Sum_grad/range/delta',\n",
       " 'gradients_1/Sum_grad/range',\n",
       " 'gradients_1/Sum_grad/Fill/value',\n",
       " 'gradients_1/Sum_grad/Fill',\n",
       " 'gradients_1/Sum_grad/DynamicStitch',\n",
       " 'gradients_1/Sum_grad/Maximum/y',\n",
       " 'gradients_1/Sum_grad/Maximum',\n",
       " 'gradients_1/Sum_grad/floordiv',\n",
       " 'gradients_1/Sum_grad/Reshape',\n",
       " 'gradients_1/Sum_grad/Tile',\n",
       " 'gradients_1/mul_grad/Shape',\n",
       " 'gradients_1/mul_grad/Shape_1',\n",
       " 'gradients_1/mul_grad/BroadcastGradientArgs',\n",
       " 'gradients_1/mul_grad/mul',\n",
       " 'gradients_1/mul_grad/Sum',\n",
       " 'gradients_1/mul_grad/Reshape',\n",
       " 'gradients_1/mul_grad/mul_1',\n",
       " 'gradients_1/mul_grad/Sum_1',\n",
       " 'gradients_1/mul_grad/Reshape_1',\n",
       " 'gradients_1/mul_grad/tuple/group_deps',\n",
       " 'gradients_1/mul_grad/tuple/control_dependency',\n",
       " 'gradients_1/mul_grad/tuple/control_dependency_1',\n",
       " 'gradients_1/Log_grad/Inv',\n",
       " 'gradients_1/Log_grad/mul',\n",
       " 'gradients_1/add_grad/Shape',\n",
       " 'gradients_1/add_grad/Shape_1',\n",
       " 'gradients_1/add_grad/BroadcastGradientArgs',\n",
       " 'gradients_1/add_grad/Sum',\n",
       " 'gradients_1/add_grad/Reshape',\n",
       " 'gradients_1/add_grad/Sum_1',\n",
       " 'gradients_1/add_grad/Reshape_1',\n",
       " 'gradients_1/add_grad/tuple/group_deps',\n",
       " 'gradients_1/add_grad/tuple/control_dependency',\n",
       " 'gradients_1/add_grad/tuple/control_dependency_1',\n",
       " 'gradients_1/layer1/Softmax_grad/mul',\n",
       " 'gradients_1/layer1/Softmax_grad/Sum/axis',\n",
       " 'gradients_1/layer1/Softmax_grad/Sum',\n",
       " 'gradients_1/layer1/Softmax_grad/Reshape/shape',\n",
       " 'gradients_1/layer1/Softmax_grad/Reshape',\n",
       " 'gradients_1/layer1/Softmax_grad/sub',\n",
       " 'gradients_1/layer1/Softmax_grad/mul_1',\n",
       " 'gradients_1/layer1/h_grad/BiasAddGrad',\n",
       " 'gradients_1/layer1/h_grad/tuple/group_deps',\n",
       " 'gradients_1/layer1/h_grad/tuple/control_dependency',\n",
       " 'gradients_1/layer1/h_grad/tuple/control_dependency_1',\n",
       " 'gradients_1/layer1/MatMul_grad/MatMul',\n",
       " 'gradients_1/layer1/MatMul_grad/MatMul_1',\n",
       " 'gradients_1/layer1/MatMul_grad/tuple/group_deps',\n",
       " 'gradients_1/layer1/MatMul_grad/tuple/control_dependency',\n",
       " 'gradients_1/layer1/MatMul_grad/tuple/control_dependency_1',\n",
       " 'beta1_power_1/initial_value',\n",
       " 'beta1_power_1',\n",
       " 'beta1_power_1/Assign',\n",
       " 'beta1_power_1/read',\n",
       " 'beta2_power_1/initial_value',\n",
       " 'beta2_power_1',\n",
       " 'beta2_power_1/Assign',\n",
       " 'beta2_power_1/read',\n",
       " 'zeros_6',\n",
       " 'layer1/W/Adam',\n",
       " 'layer1/W/Adam/Assign',\n",
       " 'layer1/W/Adam/read',\n",
       " 'zeros_7',\n",
       " 'layer1/W/Adam_1',\n",
       " 'layer1/W/Adam_1/Assign',\n",
       " 'layer1/W/Adam_1/read',\n",
       " 'zeros_8',\n",
       " 'layer1/b/Adam',\n",
       " 'layer1/b/Adam/Assign',\n",
       " 'layer1/b/Adam/read',\n",
       " 'zeros_9',\n",
       " 'layer1/b/Adam_1',\n",
       " 'layer1/b/Adam_1/Assign',\n",
       " 'layer1/b/Adam_1/read',\n",
       " 'Adam_1/learning_rate',\n",
       " 'Adam_1/beta1',\n",
       " 'Adam_1/beta2',\n",
       " 'Adam_1/epsilon',\n",
       " 'Adam_1/update_layer1/W/ApplyAdam',\n",
       " 'Adam_1/update_layer1/b/ApplyAdam',\n",
       " 'Adam_1/mul',\n",
       " 'Adam_1/Assign',\n",
       " 'Adam_1/mul_1',\n",
       " 'Adam_1/Assign_1',\n",
       " 'Adam_1',\n",
       " 'ArgMax/dimension',\n",
       " 'ArgMax',\n",
       " 'ArgMax_1/dimension',\n",
       " 'ArgMax_1',\n",
       " 'Equal',\n",
       " 'Cast',\n",
       " 'Rank_2',\n",
       " 'range_2/start',\n",
       " 'range_2/delta',\n",
       " 'range_2',\n",
       " 'Mean_2',\n",
       " 'init_1']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We first get the graph that we used to compute the network\n",
    "g = tf.get_default_graph()\n",
    "\n",
    "# And can inspect everything inside of it\n",
    "[op.name for op in g.get_operations()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the names of the operations, we see there is one `linear/W`.  But this is the `tf.Operation`.  Not the `tf.Tensor`.  The tensor is the result of the operation.  To get the result of the operation, we simply add \":0\" to the name of the operation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "W = g.get_tensor_by_name('layer1/W:0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the existing session to compute the current value of this tensor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(784, 10)\n"
     ]
    }
   ],
   "source": [
    "W_arr = np.array(W.eval(session=sess))\n",
    "print(W_arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we have our tensor!  Let's try visualizing every neuron, or every column of this matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/EcgZm47InLF5oh32tKfXtOOR+u67ayHBGTcOYoNRnh4oT4+UJwf33Xc22OES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Xz/uo7itPgtluaz0K1mvRXRwYkV7bUWbdrN0ceLmCOHYyqeuXg2uefZXJQ16YVOlIkqQcGd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5k6NaztgOWwzUPWaasfu/PUFR6vcJ5Y3jyC//8i/zMz/zM3z2s5/l+vqas7MzRITf+I3f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\nnzQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121a7b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 10, figsize=(20, 3))\n",
    "for col_i in range(10):\n",
    "    ax[col_i].imshow(W_arr[:, col_i].reshape((28, 28)), cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're going to use the `coolwarm` color map, which will use \"cool\" values, or blue-ish colors for low values.  And \"warm\" colors, red, basically, for high values.  So what we begin to see is that there is a weighting of all the input values, where pixels that are likely to describe that number are being weighted high, and pixels that are not likely to describe that number are being weighted low.  By summing all of these multiplications together, the network is able to begin to predict what number is in the image.  This is not a very good network though, and the representations it learns could still do a much better job.  We were only right about 93% of the time according to our accuracy.  State of the art models will get about 99.9% accuracy.\n",
    "\n",
    "<a name=\"convolutional-networks\"></a>\n",
    "## Convolutional Networks\n",
    "\n",
    "To get better performance, we can build a convolutional network.  We've already seen how to create a convolutional network with our unsupervised model.  We're going to make the same modifications here to help us predict the digit labels in MNIST.\n",
    "\n",
    "### Defining the Network\n",
    "\n",
    "I'll first reset the current graph, so we can build a new one.  We'll use tensorflow's nice helper function for doing this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.python.framework.ops import reset_default_graph\n",
    "reset_default_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just to confirm, let's see what's in our graph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We first get the graph that we used to compute the network\n",
    "g = tf.get_default_graph()\n",
    "\n",
    "# And can inspect everything inside of it\n",
    "[op.name for op in g.get_operations()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great.  Empty.\n",
    "\n",
    "Now let's get our dataset, and create some placeholders like before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# We'll have placeholders just like before which we'll fill in later.\n",
    "ds = datasets.MNIST(one_hot=True, split=[0.8, 0.1, 0.1])\n",
    "X = tf.placeholder(tf.float32, [None, 784])\n",
    "Y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `X` is currently `[batch, height*width]`, we need to reshape to a\n",
    "4-D tensor to use it in a convolutional graph.  Remember, in order to perform convolution, we have to use 4-dimensional tensors describing the:\n",
    "\n",
    "`N x H x W x C`\n",
    "\n",
    "We'll reshape our input placeholder by telling the `shape` parameter to be these new dimensions and we'll use `-1` to denote this dimension should not change size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_tensor = tf.reshape(X, [-1, 28, 28, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll now setup the first convolutional layer.  Remember that the weight matrix for convolution should be\n",
    "\n",
    "`[height x width x input_channels x output_channels]`\n",
    "\n",
    "Let's create 32 filters.  That means every location in the image, depending on the stride I set when we perform the convolution, will be filtered by this many different kernels.  In session 1, we convolved our image with just 2 different types of kernels.  Now, we're going to let the computer try to find out what 32 filters helps it map the input to our desired output via our training signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "filter_size = 5\n",
    "n_filters_in = 1\n",
    "n_filters_out = 32\n",
    "W_1 = tf.get_variable(\n",
    "    name='W',\n",
    "    shape=[filter_size, filter_size, n_filters_in, n_filters_out],\n",
    "    initializer=tf.random_normal_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bias is always `[output_channels]` in size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b_1 = tf.get_variable(\n",
    "    name='b',\n",
    "    shape=[n_filters_out],\n",
    "    initializer=tf.constant_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can build a graph which does the first layer of convolution:\n",
    "We define our stride as `batch` x `height` x `width` x `channels`.  This has the effect of resampling the image down to half of the size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h_1 = tf.nn.relu(\n",
    "    tf.nn.bias_add(\n",
    "        tf.nn.conv2d(input=X_tensor,\n",
    "                     filter=W_1,\n",
    "                     strides=[1, 2, 2, 1],\n",
    "                     padding='SAME'),\n",
    "        b_1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And just like the first layer, add additional layers to create a deep net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_filters_in = 32\n",
    "n_filters_out = 64\n",
    "W_2 = tf.get_variable(\n",
    "    name='W2',\n",
    "    shape=[filter_size, filter_size, n_filters_in, n_filters_out],\n",
    "    initializer=tf.random_normal_initializer())\n",
    "b_2 = tf.get_variable(\n",
    "    name='b2',\n",
    "    shape=[n_filters_out],\n",
    "    initializer=tf.constant_initializer())\n",
    "h_2 = tf.nn.relu(\n",
    "    tf.nn.bias_add(\n",
    "        tf.nn.conv2d(input=h_1,\n",
    "                 filter=W_2,\n",
    "                 strides=[1, 2, 2, 1],\n",
    "                 padding='SAME'),\n",
    "        b_2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4d -> 2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We'll now reshape so we can connect to a fully-connected/linear layer:\n",
    "h_2_flat = tf.reshape(h_2, [-1, 7 * 7 * n_filters_out])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a fully-connected layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# NOTE: This uses a slightly different version of the linear function than the lecture!\n",
    "h_3, W = utils.linear(h_2_flat, 128, activation=tf.nn.relu, name='fc_1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And one last fully-connected layer which will give us the correct number of outputs, and use a softmax to expoentially scale the outputs and convert them to a probability:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# NOTE: This uses a slightly different version of the linear function than the lecture!\n",
    "Y_pred, W = utils.linear(h_3, n_output, activation=tf.nn.softmax, name='fc_2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<TODO: Draw as graphical representation>\n",
    "\n",
    "### Training the Network\n",
    "\n",
    "The rest of the training process is the same as the previous network.  We'll define loss/eval/training functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cross_entropy = -tf.reduce_sum(Y * tf.log(Y_pred + 1e-12))\n",
    "optimizer = tf.train.AdamOptimizer().minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monitor accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(Y_pred, 1), tf.argmax(Y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And create a new session to actually perform the initialization of all the variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we'll train in minibatches and report accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.946571\n",
      "0.963714\n",
      "0.966714\n",
      "0.969714\n",
      "0.973571\n",
      "0.972143\n",
      "0.971714\n",
      "0.975\n",
      "0.969429\n",
      "0.976857\n",
      "0.997429\n"
     ]
    }
   ],
   "source": [
    "batch_size = 50\n",
    "n_epochs = 10\n",
    "for epoch_i in range(n_epochs):\n",
    "    for batch_xs, batch_ys in ds.train.next_batch():\n",
    "        sess.run(optimizer, feed_dict={\n",
    "            X: batch_xs,\n",
    "            Y: batch_ys\n",
    "        })\n",
    "    valid = ds.valid\n",
    "    print(sess.run(accuracy,\n",
    "                   feed_dict={\n",
    "                       X: valid.images,\n",
    "                       Y: valid.labels\n",
    "                   }))\n",
    "\n",
    "# Print final test accuracy:\n",
    "test = ds.test\n",
    "print(sess.run(accuracy,\n",
    "               feed_dict={\n",
    "                   X: test.images,\n",
    "                   Y: test.labels\n",
    "               }))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<TODO: Fun timelapse of waiting>\n",
    "\n",
    "### Inspecting the Trained Network\n",
    "\n",
    "Let's take a look at the kernels we've learned using the following montage function, similar to the one we've been using for creating image montages, except this one is suited for the dimensions of convolution kernels instead of 4-d images.  So it has the height and width first, unlike images which have batch then height then width.  We'll use this function to visualize every convolution kernel in the first and second layers of our network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1210835c0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UAACAgRIFAABgoEQBAAAYKFEAAAAGShQAAICBEgUAAGCgRAEAABjiQ30Bg3G4bJSdnRHb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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x120e6e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from libs.utils import montage_filters\n",
    "W1 = sess.run(W_1)\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(montage_filters(W1), cmap='coolwarm', interpolation='nearest')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What we're looking at are all of the convolution kernels that have been learned.  Compared to the previous network we've learned, it is much harder to understand what's happening here.  But let's try and explain these a little more.  The kernels that have been automatically learned here are responding to edges of different scales, orientations, and rotations.  It's likely these are really describing parts of letters, or the strokes that make up letters.  Put another way, they are trying to get at the \"information\" in the image by seeing what changes.\n",
    "\n",
    "That's a pretty fundamental idea.  That information would be things that change.  Of course, there are filters for things that aren't changing as well. Some filters may even seem to respond to things that are mostly constant.  However, if our network has learned a lot of filters that look like that, it's likely that the network hasn't really learned anything at all.  The flip side of this is if the filters all look more or less random.  That's also a bad sign.\n",
    "\n",
    "Let's try looking at the second layer's kernels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x110508eb8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Zn/0UF0+nmAWN6+KbWExYMFsoUzWMgzUa3/zvAXioX6MUtYh0m936dWJRo60t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5GX7AgVjDkfOo7oB//OIuTmvClVwL2xH88nydYBzwtPI6x2vfINSzNKMau/Ysm70Fto3pCUu3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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1210a89b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "W2 = sess.run(W_2)\n",
    "plt.imshow(montage_filters(W2 / np.max(W2)), cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's really difficult to know what's happening here.  There are many more kernels in this layer.  They've already passed through a set of filters and an additional non-linearity.  How can we really know what the network is doing to learn its objective function?  The important thing for now is to see that most of these filters are different, and that they are not all constant or uniformly activated.  That means it's really doing something, but we aren't really sure yet how to see how that effects the way we think of and perceive the image.  In the next session, we'll learn more about how we can start to interrogate these deeper representations and try to understand what they are encoding.  Along the way, we'll learn some pretty amazing tricks for producing entirely new aesthetics that eventually led to the \"deep dream\" viral craze.\n",
    "\n",
    "<a name=\"savingloading-models\"></a>\n",
    "# Saving/Loading Models\n",
    "\n",
    "Tensorflow provides a few ways of saving/loading models.  The easiest way is to use a checkpoint.  Though, this really useful while you are training your network.  When you are ready to deploy or hand out your network to others, you don't want to pass checkpoints around as they contain a lot of unnecessary information, and it also requires you to still write code to create your network.  Instead, you can create a protobuf which contains the definition of your graph and the model's weights.  Let's see how to do both:\n",
    "\n",
    "<a name=\"checkpoint\"></a>\n",
    "## Checkpoint\n",
    "\n",
    "Creating a checkpoint requires you to have already created a set of operations in your tensorflow graph.  Once you've done this, you'll create a session like normal and initialize all of the variables.  After this, you create a `tf.train.Saver` which can restore a previously saved checkpoint, overwriting all of the variables with your saved parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "sess = tf.Session()\n",
    "init_op = tf.global_variables_initializer()\n",
    "saver = tf.train.Saver()\n",
    "sess.run(init_op)\n",
    "if os.path.exists(\"model.ckpt\"):\n",
    "    saver.restore(sess, \"model.ckpt\")\n",
    "    print(\"Model restored.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating the checkpoint is easy.  After a few iterations of training, depending on your application say between 1/10 of the time to train the full model, you'll want to write the saved model.  You can do this like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved in file: ./model.ckpt\n"
     ]
    }
   ],
   "source": [
    "save_path = saver.save(sess, \"./model.ckpt\")\n",
    "print(\"Model saved in file: %s\" % save_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"protobuf\"></a>\n",
    "## Protobuf\n",
    "\n",
    "The second way of saving a model is really useful for when you don't want to pass around the code for producing the tensors or computational graph itself.  It is also useful for moving the code to deployment or for use in the C++ version of Tensorflow.  To do this, you'll want to run an operation to convert all of your trained parameters into constants.  Then, you'll create a second graph which copies the necessary tensors, extracts the subgraph, and writes this to a model.  The summarized code below shows you how you could use a checkpoint to restore your models parameters, and then export the saved model as a protobuf."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='./'\n",
    "ckpt_name = './model.ckpt'\n",
    "fname = 'model.tfmodel'\n",
    "dst_nodes = ['Y']\n",
    "g_1 = tf.Graph()\n",
    "with tf.Session(graph=g_1) as sess:\n",
    "    x = tf.placeholder(tf.float32, shape=(1, 224, 224, 3))\n",
    "    # Replace this with some code which will create your tensorflow graph:\n",
    "    net = create_network()\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess, ckpt_name)\n",
    "    graph_def = tf.python.graph_util.convert_variables_to_constants(\n",
    "        sess, sess.graph_def, dst_nodes)\n",
    "g_2 = tf.Graph()\n",
    "with tf.Session(graph=g_2) as sess:\n",
    "    tf.train.write_graph(\n",
    "        tf.python.graph_util.extract_sub_graph(\n",
    "            graph_def, dst_nodes), path, fname, as_text=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you wanted to import this model, now you wouldn't need to refer to the checkpoint or create the network by specifying its placeholders or operations.  Instead, you'd use the `import_graph_def` operation like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"model.tfmodel\", mode='rb') as f:\n",
    "    graph_def = tf.GraphDef()\n",
    "    graph_def.ParseFromString(f.read())\n",
    "\n",
    "tf.import_graph_def(net['graph_def'], name='model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"wrap-up\"></a>\n",
    "# Wrap Up\n",
    "\n",
    "In the next session, we'll learn some very powerful techniques for exploring the representations learned by these kernels, and how we can better understand what they are learning.  We'll look at state of the art deep networks for image recognition and interrogate what they've learned using techniques that led the public to Deep Dream.\n",
    "\n",
    "<a name=\"reading\"></a>\n",
    "# Reading\n",
    "\n",
    "Bourlard, H.; Kamp, Y. (1988). \"Auto-association by multilayer perceptrons and singular value decomposition\". Biological Cybernetics 59 (4–5): 291–294.\n",
    "\n",
    "G. E. Hinton, R. R. Salakhutdinov. Reducing the Dimensionality of Data with Neural Networks.  Science, 28 Jul 2006. Vol. 313, Issue 5786, pp. 504-507. \n",
    "DOI: 10.1126/science.1127647. http://science.sciencemag.org/content/313/5786/504.abstract\n",
    "\n",
    "Bengio, Y. (2009). \"Learning Deep Architectures for AI\". Foundations and Trends in Machine Learning 2. doi:10.1561/2200000006\n",
    "\n",
    "Vincent, Pascal; Larochelle, Hugo; Lajoie, Isabelle; Bengio, Yoshua; Manzagol, Pierre-Antoine (2010). \"Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion\". The Journal of Machine Learning Research 11: 3371–3408.\n",
    "\n",
    "Auto-Encoding Variational Bayes, Kingma, D.P. and Welling, M., ArXiv e-prints, 2013 http://arxiv.org/abs/1312.6114"
   ]
  }
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